Assisting Human Decisions in Document Matching
Authors: Joon Sik Kim
Valerie Chen
Danish Pruthi
Nihar B Shah
Ameet Talwalkar
Venue: Accepted by TMLR ()

Abstract: Many practical applications, ranging from paper-reviewer assignment in peer review to job-applicant matching for hiring, require human decision makers to identify relevant matches by combining their expertise with predictions from machine learning models. In many such model-assisted document matching tasks, the decision makers have stressed the need for assistive information about the model outputs (or the data) to facilitate their decisions. In this paper, we devise a proxy matching task that allows us to evaluate which kinds of assistive information improve decision makers’ performance (in terms of accuracy and time). Through a crowdsourced (N = 271 participants) study, we find that providing black-box model explanations reduces users’ accuracy on the matching task, contrary to the commonly-held belief that they can be helpful by allowing better understanding of the model. On the other hand, custom methods that are designed to closely attend to some task-specific desiderata are found to be effective in improving user performance. Surprisingly, we also find that the users’ perceived utility of assistive information is misaligned with their objective utility (measured through their task performance).

Published in Transactions on Machine Learning Research (07/2023)
Assisting Human Decisions in Document Matching
Joon Sik Kim Cornegie Mellon University
joonkim@@cmaucom edu
Valerie Chen Cornegie Mellon University
vatericchen@ommu.com edu
Danish Pruthi
donishp Wrisc ac. in
Indian Institute of Science, Bangalore
Nihar B. Shah
vähars@andrew.co cww. edu
Cornegie Mellon University
Ameet Talwalkar
Labwalkar@ermae eduu
Carnegie Mellon University
Reviewed on OpenReview: https: /ttpp:://ppnneei opwnrevien...e.... net/ forum? id= Brg8AR=HAQ
Abstract
Many practical applications, ranging from paper-reviewer assignment in peer review to job- applicant matching for hiring, require human decision makers to identify relevant matches by combining their expertise with predictions from machine learning models. In many such model-assisted document matching tasks, the decision makers have stressed the need for assistive information about the model outputs (or the data) to facilitate their decisions. In this paper, devise proxy matching task that allows to evaluate which kinds of assistive information improve decision makers' performance (in terms of accuracy and time). Through a crowdsourced (N = 271 participants) study, we find that providing black-box model explanations ions reduces users' accuracy on the matching lask, contrary to the commonly-held belief that they can be helpful by allowing better understanding of the model. On the other hand, custom methods that are designed closely attend to some task-specific desiderata found to be effective in improving user performance. Surprisingly, we also find that the users' perceived utility of assistive information misaligned with their objective utility (measured through their task performance).
1 Introduction
An important application in which human decision makers play a critical role, is document matching, iee,, when a query document needs to be matched to one of the many candidale documents from larger pool based on their relevance. Concrete instances of this setup include; academic peer review, where meta- reviewers associate editors in journals (e.g., htttps://jmle http:::/jjllrrrr//taeee wtwp.//rrrrrrmmm/raaueeeeeee html) or area chairs conferences (Shah) 2022) or program directors conducting proposal reviews (Kerzendorf al., 2020) are asked to assign
more candidate reviewers to submitted papers with relevant expertise based on their previously published work (illustrated in Figure D solid arrows); recruitment, where recruiters screen through list of resumes from candidate applicants for an available position at the company (Schumann et al. 2020 Poovizhi et. al. 2022); and plagiarism delection, where governing members (e.g., ethics board members of a conference, instructors of a course) review submissions to determine the degree of plagiarism (Foltýnek et al,) 2019). Bocause the pool of candidate documents is typically large and the decision makers have limited time, they first use automated matching models to pre-screen the candidate documents. These matching models typically base their screening affinity scores, which measure the relevance of each candidate document. to

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Published in Transactions on Machine Learning Research (07/202) 2023)
Submitted Paper
Matching Model
0.81
0.79
0.78
0.79
0.78
Pool candidate
reviewers
Pre-screened reviewer profiies with affinity scores
Pro -o--soooddd reviower profiles with highlighted information
Highlight relevant informaiion
candidate documents
An example document matching application of peer review. For each submitted paper, the matching model pre-screens list of candidate reviewers via allinity scores (solid arrows). Mela-reviewers, typically under time constraint, then select the best match the submitted paper among the pre-screened reviewer (box with solid line). We study whether providing additional assistive information, namely highlighting potentially relevant information in the candidate documents, can help the meta-reviewers make better decisions (dotted arrows and boxes). We do so by focusing proxy matching task crowdsourcing platform that is representative of real- world applications not limited to peer review, including recruitment and plagiarism detection which follow the similar setup with different documents and decision makers.
Figure 1:
the query document (Alzahrani et al. 2012 Charlin Zemel 2013 Cohan al. 2020 Li et al. 2021). The human decision makers subsequently determine the matching document, taking both their expertise and the affinity scores computed by the matching models into account. Such intervention by human decision makers is required for such tasks, as often times either errors made by the models are so consequential that they warrant human oversight, or the overall performance can be considerably improved by incorporating the domain knowledge of human experts.
Despite the growing prevalence of automated matching models and human decision makers working jointly for such practical matching tasks, humans generally find it difficult to completely rely on the models due to a lack of assistive information other than the models' output itself. For instance, in peer review, 20% of the meta-reviewers from past NLP conferences found the affinity scores from the matching model to be "not very useful or not useful al all" in recent survey (Thorn Jakobsen & Rogers 2022). The survey also reports that the affinity scores rank the least important for the respondents, compared to more tangible and structured information about the candidate reviewers such as whether they have worked on similar tasks, datasets, or methods. Additionally, the survey finds that providing just the affinity scores increases the meta-reviewers' workload as they "have identify the information they need from glance at the reviewers' publication record." " and "are presented with little structured information about the reviewers." " Similarly, in hiring, the recruiters need to manually evaluate more profiles further down the search result pages due to too generalized matches suggested by the model (Li et al. 2021
To address the lack of additional assistive information in the document matching setup, we conduct the first evaluation of what additional information can help the human decision makers to find matches accurately and quickly, compared to when given no additional information (Figure [ dotted arrows). To do so, we first design proxy task of summary-article matching that representative of the general setup so that several methods providing different types of assistive information can be readily tested at scale via erowdsourced users (Section 3.1p The choice of proxy task addresses the logistical difficulty and expenses of directly experimenting with real domain-specific decision makers.
On this proxy task, we explore different classes of methods that have been previously suggested as tools for users to understand model outputs or document content. To standardize the format of assistance, we focus on methods that highlight assistive information within the candidate documents that the decision makers can utilize for matching (Section 3.2
SHAP Lundberg & Lee 2017). , popular black-bor model explanation Doshi Velez Kim 2017
Chen al. 2022). highlights input tokens in the document. that contribute both positively and
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negatively to the affinity scores. The utility of SHAP several concrete downstream tasks remain controversial with conflicting results (Kaur et al. 2020 Jesus et al. 2021 Amarasinghe et al. 2022
and has yet to be evaluated for its effectiveness in document matching.
BERTSum Liu Lapata 2019), a state-of-the-art text summarization method, which highlights key sentences in the candidate documents to help reduce the user's cognitive load for the task.
Two task-specific methods, that design ourselves (Section 3.2 to highlight details in the candidate documents relevant to the details in the query (by using sentence and phrase-level similarity measures).
With assistive information provided by these methods as treatments, and a control group provided with just the affinity scores and no additional assistive information, we conduct a pre-registered user study (with 271 participants) on crowdsourcing platform The study finds that (Section
Despite its usage in numerous applications, SHAP decreases the participants' matching performance compared to the control group.
Contrary to the expectation that summarizing long articles could improve task efficiency, the summaries generated by BERTSum adversely impact the participants. Participants take longer to finish and are less accurate compared the control group.
Our specffc methods, which tailored to better identify details useful for the task, help the participants to be quicker and more accurate compared to the control group.
An overwhelming number of participants in all treatment groups perceive that the highlighted information is helpful, whereas the quantitative performance (accuracy and time) says otherwise.
The results suggest the benefits of designing task-specific assistive tools over general black-box solutions, and highlight the importance of quantitative evaluation of the methods' utility that is grounded on specific task over subjective user perceptions (Chen et al. 2022). The code used for the study is available at https: //ggtt/ wttp.///st.rrrooooueettaaaaaaaa.
2 Related Work
Prior Evaluation of Assistive Information. We discuss how our proposed evaluation of different types of assistive information, which include affinity scores, black-box model explanations, and text summaries, differs from how they have been previously evaluated.
Affinity scores, computed by comparing the similarity of representations learned by language models, are commonly used in practice to rank or filter the candidate documents (Mimno & McCallum 2007 Rodriguez & Bollen 2008 Charlin & Zemel 2013 Tran et al. 2017; Wieting et al. 2019 Cohan et al., 2020 Their quality has been evaluated both with or without human decision makers: some may evaluate them based on the user's self-reported confidence score (Mimno & McCallum, 2007), while others may use performance from proxy tasks like document topic classífication, where higher test. accuracy of the classification model using the learned representation indicates better ability to reflect more meaningful components in the documents (Cohan et al. 2020). However, the utility of affinity scores for assisting human decision makers for the document matching task is less studied.
While information provided by black-box model explanations have been evaluated for their utility to assist human dccision makers in various downstream tasks, the results have been lackluster. On the deception detection lask, where users are asked to determine if a given hotel review is fake or not, prior work have shown that only some explanation methods improve user's task performance [Lai & Tan, 2019 Lai et al.) 2020). Arora et al. (2022) further show that none of the off-the-shelf explanations help the users better understand the model's decisions on the task. On more common NLP tasks like sentiment classification
Prre-rngittration document is available here: https://aapreeicctal.rrr////
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and question-answeeiing, providing explanations to the users decreases the task performance compared to providing nothing when the model's prediction is incorrect (Bansal et al., 2021). For the fraud detection task with domain experts, providing some model explanations showed conflicting effects on improving the performance (Jesus et al., 20021 Amarasinghe et al.) 2022). In this work, we expand user evaluations of black-box model explanations to the document matching lask and propose alternalives that could be more helpful.
Summaries generated by text summarization models {Lewis et al. 2020 Liu & Lapata 2019 Shleifer & Rush Zhang et al. 2020) are typically either evaluated by quantitative metrics like ROUGE (i.e., statistics based on the amount of overlapping tokens between the generated text and the target text) with respect to the annotated ground-truth summary in a standardized dataset, or by a human's 's subjective rating of the quality. To the best of our knowledge, the usefulness of these automatically summarized information to the human decision makers in concrete downstream tasks rarely studied. Even for a few applied works that utilize these methods to practical documents in legal or business domains, the final evaluations do not explore beyond these task-independent metrics (Elnaggar et al., 2018: Bansal et al., 2019 Huang et al.] 2020). In this work, we explicitly evaluate whether the generated summaries can help improve the decision makers' task performance in document matching.
2020
Practical Concerns in Document Matching Applications. There are a number of real-world document. matching applications including peer review, hiring, and plagiarism detection. For each application, we discuss practical issues that have been raised by users that can be mitigated by providing more assistive information about the data and the model.
In scientific peer review, submitted papers need to be matched to appropriate reviewers with proper expertise experience in the paper's 's subject area. First, set of candidate reviewers are identified using alfinity scoring model based on representations learned by language models (Charlin & Zemel 2013 Mimno & McCallum 2007 Rodriguez & Bollen 2008 Tran et al. 2017 Wieting et al.) 2019: Cohan et al.) 2020). Additional information such as reviewer bids or paper/reviewer subject areas may also be elicited (Shah et al., 2018: Meir et al.] 2020 Fiez et al., 2020). Based on this information, meta-reviewers may either be asked directly assign one or more reviewers to each paper, or to modify the assignment that has been already made as they see appropriate. For example, in the journal Transactions
Machine Learning Rescarch, for any submitted paper the mela-reviewer (action editor) is shown a list of all non-conflicted reviewers sorted according to the affinity scores. The meta-reviewer may also click on any potential reviewer's 's name to see their website or list of publication. The meta-reviewer is then required to assign three reviewers to the paper based on this information. However, a recent survey of meta-reviewers from past NLP conferences reveal that the affinity scores alone are not as useful, and most respondents prefer to see more tangible and structured information about the reviewers (Thorn Jakobsen & Rogers 2022
In hiring, many companies resort to various algorithmic tools to efficiently filter and search for suitable candidates for given job listing (Fernández & Fernández 2019 Black & van Esch 2020 Poovizhi et al. 2022). Many recruiters, while using these tools, express difficulties in reconciling a mismatch between algorithmic results and the recruiter's own assessments. This is mainly attributed to "too generalized and imprecise" relevant matches suggested by the model, which lead to more "manually evaluating more profiles further down the search result pages" increasing the task completion time (Li et al.) (202)). Also, the general lack of understanding about. the algorithmic assessments makes the recruiters more reluctant to adopt them.
In plagiarism detection, many existing software tools aim to reduce the governing members' workload by providing detailed information about the match, c.g., what specific parts of the query document are identical or similar to parts of the candidate documents. However, their performance in identifying various forms of plagiarism (e.g., ones involving paraphrasing or cross-language references) is still limited (Jiffriya et al.) 2021). Also many existing tools lack user-friendly presentation of information that can better assist the task (Foltynck et al., (2020). As the governing members need to ultimately assess the proposed evidence by the model to determine the degree of penalty Folltyne et al. 2019), additional assistive information about. the match may improve their experience.
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Summary
Query Summary
Hormer Yemeni Presideut All Abdullat: Solch will leeve, source says. Ousted leader Abdu Rabo Mansour Hadi pecatiiees to retura. Next pluse, called "Uperation Renewal of Hops," will focus on potrical procese.
Affinity Scores
Article ... Score: 0.71
Article 2 Scure:: 01.71
Article 3 Score: 0.65
CNN) The U.N. Security Council voted Tuesday in fuvor of an arms emibargo on Houthis the taken large Yemen, including its capital, Sanza and of former Yemeni Pressdent Ali
CNN) A Saudi led cualition Thcssay ended lts" Operation Decisive Stona " its nearly monthong sintrike campaign
Sansn, Yomun CNN) Saudi sinsitiks over Yomen have resumed once again, two days after Studi und
minority group
compuign,
underway. Operation Kenewal of Hope will focus the political
airstrikes Tharsday targeted rebel Houthi militant positions thro parts of Sanos, Yemeni
Candidate Articles
Saudi Ambia had
Abdallah
resolation ruises cost"
aiestrikes on Houthi positions across Yomen, hoping to wipe out the Iranian- allied nibel group that overthrown the government scinod power. The Soudis say they wast to reatore the Yemeni govermment, key U. ally in the fight agsinst Queda, which kicked the (apinal by the rebels corlier this year. This month.
Ministry
uttacks
for the Houthis, according to Mark Lyall Grani,
four hours, The strikes caused no cioualties, but did
Britain's umbassador the United Nations. addition to arms cmbbargo also Jemandss that the Shiite group pull back and refrain from more viodence and incluks stactions aimaal controlling the spread terrorism, aocording
destrny three military compounds
targctod, the officiuls said. They said Studi airstrkes were also targeting Houzhi positions in provinee. Tussday, Snudi Arabio arnounced the end of its Operation Decisive Sterm.
artic accuranaly copt.iing informetioon summary?
Multiple Choice
Article
Question
O Attice
Attice
Figure 2: Interface for our summary-article matching task, an instance of the general document matching task. For each question, the participants are provided with the summary, three candidate articles select from, and affinity scores for each candidate. The articles here are abridged to save space.
3 Task Setup and Methods
In Section 3.1 we describe the design of a summary-article matching task, which is an instance of the document matching tasks. We use this task as a proxy for other document matching tasks (e.g., matching reviewers to papers in peer-review), as it is more amenable for crowdsourcing experiments at scale. The summary-article matching task addresses common difficulties encountered when directly experimenting on real-world applications like recruiting real domain-specifie decision makers (e.g., meta-reviewers in academiaa), building complex systems in practice (e.g., internal systems that govern workflows in academic conferences), and coordinating logistical issues (e.g., longer turnaround for receiving feedback for each paper assignment). Our task may also be useful for carly prototyping and validation different methods. Then in Section 3.2 we present existing and our proposed methods that provide assistive information that we evaluate with human users on the the summary-article matching task.
3.1 Instantiating Document Matching
In the general document matching task, a matching model pre-selects a set of candidate documents based on the affinity scores, which capture the relevance ween the query document, and the candidate document. These affinity scores facilitate filtering candidates from large pool of documents, but are nevertheless prone to errors. The user therefore goes over the candidate documents with the scores and selects the most relevant. candidate document. A practical concern which we would like to address is when the affinity scores from the matching model alone may not provide sufficient. information to determine match quickly and accurately. We outline how we instantiate the summary-article matching task that captures these details.
Task setup. We instantiate the general document matching tasks with a summary-articd matching task. Here, the query and candidate documents are cach sampled from human-writtee summarics and news articles in the CNN CON//Dalye Mail dataset (Hermann al.) 2015 See et al., 2017). a common NLP dataset used for summarization task. We select. this dataset because the contents accessible to a general audience, which
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Max Affinity Score Difference
between Correct and Wrong Articles
0.150
Absolute Difference
0.125
0.100
0.075
0.050
0.025
0.000
hard
easy
Distribution of affinity scores scores-compante computed by the matching model- for hard and easy questions. The box plot shows maximum absolute difference affinity scores between the correct and wrong candidate articles each of the hard and easy questions. The smaller the absolute difference is, the smaller the gap between the correct and the wrong article, making the scores less helpful identifying the correct article (e.g., for the hard questions).
Figure 3:
enables us to evaluate a variety of assistive methods by employing crowdworkers as in Lai el al. (2020) Wang & Yin (2021). So in our task, the participants are given a series of questions composed of a query summary with three candidate articles] and are asked to select an article from which the summary is generated under a time constraint (Figure
As the general document matching task, each candidate article presented with an affinity score computed by a language model, which captures the similarity between the article and the summary. The affinity scores are computed by taking cosine similarity between the final hidden representation of language model for the article and the summary (Charlin & Zemel 2013 Wu et al., 2020). We use the representations from the DistilBART [Shleifer & Rush 2020) model fine-tuned on the CNN/DailyMaii dataset.
Question types. In practice, there are some questions where the correct (document) match obvious, whereas other questions require a more thorough inspection of the specifics. For instance, in scientific peer review, a paper about a new optimization method in deep lcarning may be assigned to a broad range of candidate reviewers whose general research area. is within deep learning. However, reviewer who has worked both on optimization theory and deep learning may be better fit compared to others who have primarily worked on large-scale deep-learning based vision models. Even among the reviewers in optimization theory, the reviewers who have worked on similar type of methods to the one proposed may be better suited for the match. Such subtleties require more careful examination by the meta-reviewers.
We capture such scenarios by creating a data pool composed of two types of questions via manual inspection: easy and hard. Easy questions have candidate answers (articles) from different topies or events that are easily discernible from another, and therefore be easily matched correctly. On the other hand, hard questions have candidate articles with a shared topic that only differ in small details, requiring more careful inspection by the users.
On easy versus hard questions, the affinity scores naturally show distinctive behaviors. The gap of the scores between the correct and the wrong matches is smaller for the hard questions than for the easy ones (Figure 3 Because the scores for all candidate articles are similar to one another in the hard questions, the affinily scores are not as helpful in identifying the best match. Additionally, is more likely that the candidate article with the highest affinity score is not the correct match in the hard questions. If hypothetical user was to simply select candidate article which has the highest alfinity score by completely relying the matching model's output, they would be accurate only for 33.3 percent of the time for the hard questions, compared to 100 percent of the time for the easy questions. We believe that providing users with assistive information might be critical for improving outcomes when making decisions on the hard questions, when the model less accurate and the correct match is more difficult to find.
While the decision makers in general matching task may observe more than three candidate articles, devise simpler instantiation here to reduce the complexity the task, which will be better suited for the crowdsourcing task.

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Published in Transactions on Machine Learning Research (07/2023)
Ground-truth Article
Alternate Candidate Article
Alternate Candidate Article 2
jowtae ach ciile's cal0o00 voloxto spread exst less argenaton,, the voleane has already aropted veios this seek, incked
IcHocsile's 's Caloseo Volcoro erspted oggis Ttursday, warking the third time since last work, but tot at sevore
(OSSCChLLe' Colburu volcamo irapted tvice houre,
Geology
ttursday-
cant centineceri) sera placea, occurding the Rinistey ot Istarice and Publie Gasety- Calases scupted batee
estanl lihed aroond the orater, and Cn Lean aestoris
1:2mm
Tte eginy said it was avalugting the spectseilar nigtatine
erops Lony lncicoted wap "ateonge the
sours, the gestogicnl
base been kneping real deen away from that sonn. fents the intarice and Pablle Sadety sasd.
acocoding to the Miniassy of istacice and Pibile sacety. Acchorstios Lamved rnd slars the some FMEEED Ronts destinaciona.
openry
Astsoricies Lasoed tut asere tor the popolari fourint towta Paazto Morst and Duecto Tarei in she sauch,
chila, are popalar
authocitien tiew last weak Laxoed and asuee tia popsins
A 12-eilee (20 Rilemeter) excturier, 2008 vaa eatabliabed acound the cERSeR.
C7 (yrppin the andiniinn Lonsl 3, 1,000 resl denns of Chonlee were being lievela peerentive
20%
southeen Chiiea.
Information unique
volcanic flove....
to the ground--ruth only
Adjusted Summary
Original Summmary
Chile Calbuoo Volosno already hos propted tuice in 24 houre. Aothorities laated
popull souriet deatinahions Like Poneso
Voloano alresily
tullce weel.
to ZAs apeved kih dopel of abous 235 Lookel in soee planes, chi lean officials Antoritiess Leane an alort for tan Loucd, and bnore's Romile exolossion 2008.
leloarle Clows sain sizer leeeln to riso, fornies thousends of residonse no.
avamussed precassion.
Figure 4: Ensuring a single correct match for the questions. For an original summary sampled from the dataset, the three candidate articles appear to be all correct matches- - the critical information highlighted with different colors in the original summary is contained in all three candidate articles (lighlighted with the same colors). To resolve having multiple correct matches for the question, we mamually extract information unique to the ground-truth article only (underlined text in dotted box) and add to the adjusted summary to ensure that only the ground-truth article is the correct match for the summary.
Defining ground-truth matches. A ground-truth match for a given summary and a set of candidate articles is necessary to measure participant performance. To construct pairs of summary and candidate articles, we first sample a summary-article pair from raw dataset and consider the article as the ground-truth for the given summary. We then select two other articles from the dataset which have the highest affinity scores with respect to the given summary as the incorrect candidate articles for the given summary.
There are several instances where the two alternate candidate articles, which should be incorrect choices, may arguably be a suitable choice for the given summary. This happens because the dataset contains multiple articles covering the same event. To resolve this issue of having multiple ground-truths, we manually modify the given summary so that it is consistent only with the ground-trut article. Specifically, we manually identify unique information in the ground-truth article that is not part of the alternate candidate articles and add that information to the summary (Figure 4
3.2 Tested Methods
In this section, we describe the methods used to highlight assistive information that we evaluate in our study and how they are presented to the users.
Black-box Model Explanation. Black-box model explanations include techniques that aim to highlight, important input tokens for a model's prediction (Simonyan et al., 2013 Shrikumar al.) 2016 Sundararajan et al. 2017: Lundberg & Lee. 2017). While there are several candidates to consider, we use a widely- applied method called SHAP (Lundberg & Leel 2017). SHAP assigns attribution scores to each input token that indicate how much they contribute to the prediction output. We select SHAP from a pool of prominent explanation methods (which include Integrated Gradients (Sundararajan et al. 2017) and Input x Gradients (Shrikumar et al. 2016)) by examining how much the distribution of attribution scores deviate from random distribution of attribution scores (see Appendix A for details).
We visualize SHAP (example shown in Figure 5 third row) by highlighting the input tokens according to their attribution scores. Tokens that contribute to increasing the affinity score (i.e., those with positive attribution scores) are highlighted in cyan, while those that decrease the score (i.e., those with negative attribution scores) are highlighted in pink. The color gradients of the highlights indicate the magnitude of the attribution scores: the darker the color, the bigger the magnitude.
Extractive Summarization. Summarization mothods are trained to select key information within a large body of text. These methods can potentially help users process multiple lengthy articles in a shorter amount of time [Liu & Lapata 2019 Zhong et al. 2020 Lewis et al. 2020 Zhang et al. 2020). Summaries generated
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Query Summary
Chile's Calbuco Volcano eiresdy has exupted twice in 24 hours. Authorities issued red alert for popular destinations Like Puerto Hontt and Puerto Vares in southern Chile, Volcenie flowe made river levels to rise, foraing thousands of reaidents to be evacuated as a precaution.
(CHE)AS ash from Chile's 's Calbuco Volcano spreed cast into Argentina, geologists warned of the potential for activity Priday, Evacuations in the region involved not only people but aninals well. The volcano some places, according to the Ministry ot Interior and Public Satety. Calbuco erupted tvice in 24 hours, the geoloçical agency said early Thursday. Authorities issued Fed alert for the popular tourist towns Puerto Nontt and Puecto Varee in the south, An additionel 2,000 residents of Chenizs were being evacuated after river levels
hos already erupted twice this weak, spewing ash
depth of about 234 inchea (60 centinetara)
Key Parts
due volcanie flowa. Another person said: impressive to see an chornous mushroon cloud, with the inmense force of the volcano, and to see the ashes.
prevent.ive
(CRE)As ash from Chile's 's Calbuco Volcano spread east into Argentina, geologists worned of the potential for activity Priday, Evacustions in the region involved not has already erupted twice this keek, spewing ash to depth of about 234 inches [60 centineters) in some places, according to the Ministry of Interior and Public Safesy. Calbuco erupted tvice in 24 hours, the geological agency said early Thursday. Authorities issued zed alert for the popular tourist towns Puerto Sontt and Puerto Vares in the south, An additicnal 2,000 residents of Chanizs were being evacuated as preventiive peasure
people but animals well. The volcano
SHAP
river levels rose due to volcenic flows. = Another person said: impressive to see an enoraous mushroon gloud, with the inmense force of the yolcano, and to see the ashes.
(CNN)AS ash from Chile's a Calbuco Volcano spread east into Argentina, geologists warned of the potential more activity Priday. Evacuations in the region involved not only people but animals as well. The volcano some
has already erupted twice this week, spewing ash places, according to the Hinistry of Interior and Public Safety. Calbuco erupted twice in 24 hours, the geological agency
depth of about 23) inches (60 centimeters)
BERTSum
early Thursdays Authorities issued red alert Puarto Montt and Puarto Varas in the south. An additional 2,000 residants of Chaniza were being avacuated preventive
the popular tourist towns
after river levels impressive to see an enormous mushroon cloud, with the inmense force of the volceno, and to see the ashes,
due to volcanic flows. Another person said: 'It
Argentina, geologista warned of the potential for more activity Priday. Evacuations in the region involved not only people but animola as well. - The volcano aas already erupted twice this week, spewing ash
(AN from Chile's Calbuco Volcano spread
depth of about 23 inches (60 centineters)
Co- occurrence
places, according to the Hinistry of Interior and Public Safety, m Calbuco erupted twice in 24 hours, the geological agency said early Thuraday. Authorities issued red alert for the popular tourist towns of Poerso Montt, and Puarto Varas in the south, - An additional 2,000 reaidents
Chaniza were being avacuated as preventive neasure after river Levels rose due to volcanic flows. Another person said: 'It was impressive to see an евоmmous mushroon cloud, with
inmense force of the volcano, and see the ashes.
(CNM)AS ash from Chile's 's Calbuco Volcano spread east into Argentina, geologista warned of the potential for activity Priday. Evacuationa in region involvad not only people but animals will The volcano has already erupted twice this week, spewing ash to depth of about 23/ inches (60 centimeters) in some places, according the Ministry of Interior and Public Safety. Calbuco erupted twice in 24 hours, the goological agency said early Thursday. -. Authorities issged red alert for the popular tourist towns Puerto Nentt and Puerto Vares in the south. An additional 2,000 residents of Chanizs were being evacuated as preventive nessure efter river levels rose due to volcenic flows. " Another person said: impressive to see an enoreous mushroon cloud, with the inmense force of the volcano, and to see the ashea.
Semantic
was
Figure 5: Highlighted information using different methods on the ground-truth article of the summary-article pair example in Figure [ Highlights for "Key Parts" (second row) indicate information relevant to the query summary (first row), all of which ideally should be visibly highlighted by the methods that follow. SHAP (third row) and BERTSum (fourth row) fail to fully highlight all key parts. Critically, they fail to visibly highlight the key part about river levels rising (yellow highlights in Key Parts), the unique information that distinguishes the ground-truth from other candidate articles (as described in Figure 4 which can directly impact the participant's performance. On the other hand, our lask-specific methods, both Co-occurrence (fitth row) and Semantic (sixth row) ones, are able to visibly highlight all key parts.
by these methods are typically either abstractive (i.e., the summary is a newly-generated text that may not be part of the original text) or extractive (i.e., the summary is composed of text pieces extracted from the original text) (Hahn & Mani 2000). Because abstractive summaries are more susceptible to hallucinating information not present. the original text (Cao et al.] 2018 Maynez et al. 2020 Ji et al. 2022). we focus on evaluating extractive summarics. In particular, we use BERTSum (Liu & Lapata 2019). which achieves state-of-the-art performance on the CNN Mail dataset (Pagnoni et al. 2021 ; to extract three key sentences from the article. We visualize the extracted summary by highlighting the selected sentences from the original text with a single solid color (example shown in Figure 5 fourth row).
Task-specific Methods. The summary-article matching task requires users to accurately and quickly identify whether all details in the summary are correctly presented in each article. This is particularly challenging for hard questions, where the ground-truth can only be identified by looking at the right part of the articles due to their subtle differences. Next we propose two task-specific methods that are more tailored to addressing this challenge.
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The methods operate at sentence and phrasc-level information in the summary and candidate articles. Specifically, we select and show the top K' sentenced from each article with the highest similarity measure to each sentence in the summary R have been selected, we then show exactly-matching phrases within those selected sentences. Essentially, the methods are designed to guide the users to relevant parts in the article for each summary sentence by presenting the relevance hierarchically- - by first showing the key sentences and then the key phrases within,
To further provide more fine-grained detail on why that sentence could
We consider two versions of the method which use different similarity measures to select the sentences:
Co-occurrence method uses F1 score of ROUGE-L (Lin 2004 a common performance metric used to capture the degree of n-gram co-occurrence between two texts.
Semantic method uses the cosine similarity between the sentence representations from Sentence- BERT (Reimers & Curevych 2019), a transformer model trained for sentence-level tasks. These scores are more sensitive to semantic similarities among texts like paraphrased components that may not be effectively captured by ROUGE-L.
Once we select K sentences based on the similarity measures, we visualize them using different colors to differentiate sentences in the article related to different sentence in the summary. Like before, we use color gradients to indicate the magnitude of the similarity score for each sentence (the higher the similarity, the darker the color). We then color the exactly-matching phrases using the darkest shade. For instance, in Figure 5 (fifth and sixth rows), the pink, blue and yellow highlights indicate relevant parts to first, second, and third sentence in the summary respectively. We include additional examples from each of the explored methods in Appendix
4 Experiments
We run pre-registerd user study on the summary-article matching task introduced in Section 3.1 to evaluate the methods described in Section 3.2 as treatment conditions. In this section, we outline the details of the user study (Section 4.1p, followed by our main hypotheses (Section 4.2p and results (Sections 4.3
4.1 User Study Design
We present 16 questions to each participant. The 16 questions comprise 4 easy and 12 hard questions in random order. Participants complete all questions in one sitting. For each question, participants see a query summary followed by three longer candidate articles (see Figure 2 for an example). To incorporate the time constraints typical decision makers may face in practical settings, as similarly done in (Pier et al.] (017). we limit participants to spend minutes to answer each question, after which they automatically see the next question. We offer bonus payments to encourage high-quality responses in terms of both accuracy and time (more details in Appendix D.4.
We recruit 275 participants from a balanced pool of adult males and females located in the U.S. with minimum approval ratings of 90% on Prolific www..prolific.oo co), with diverse demographic background (more details in Appendix D.2p. The sample size determined from Monte Carlo power analysis based on data collected from a separate pilot study, for a statistical power above 0.8 (more details in Appendix D.I. Each participant. is then randomly assigned to one of five groups:
"We pick K = 3, but this can be tuned for different levels of detail, depending the length of the summary or the article. "Note that one could alternatively consider applying SHAP to the sentence-level information from the summary instead of the entire summary. While seemingly providing sentence-level application of SHAP would be unconventional and inconsistent with SHAP's intended use. Indeed, SHAP is designed to explain a model's prediction, and in our set up the model makes prediction for the affinity score between the entire summary and the artide. Hence, the most natural and consistent way of using SHAP is applying it to the entire input. For text summarization method, similar argumeut holds: the method summarizes longer text by extracting a subset of information and therefore meant to take in the entire document content as the input. á Prrerreiittration document is available httpa://sapredsceet org/LMMM____M
apples-to-apples comparison to the sentence-level task-specific methods,

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Control: participants the basic information (summary, articles, affinity scores)
SHAP: participants see the basic information + highlights from SHAP
BERTSum: participants see the basic information + highlights from BERTSum
Co-occurrence: participants see the basic information + highlights from Co-occurrence method
Semantic: participants see the basic information + highlights from Semantic method
We include two attention check questions in the study in addition to the 16 questions above. 271 out of 275 participants pass both attention-check questions, and we exclude responses from the 4 non-qualifying participants from our further analysis. We include mode details about the user study in Appendix D
4.2 Main Hypotheses
We pose the following null hypotheses with two-sided alternatives about the mean accuracy of participants the hard questions using different kinds of assistive information:
HH1) The mean accuracy of participants using SHAP is not different from that of the control.
(H2) The mean accuracy of participants using BERTSum is not different from that of the control.
(H3) The mean accuracy of participants using Co-occurrence method is not different from the control.
(H4)
The mean accuracy of participants using Semantic method not different from the control.
To test each hypothesis, we compare the accuracy of the participants in different treatment settings against the control group with two-tailed permutation tests, where the test statistic is the difference in the mean accuracy. We account for multiple comparisons with Sidak correction (iidk 1967 for the family-wise error rate of 0.05.
4.3
Results
We now discuss the participants' task accuracy (Section 4.3.1p completion time (Section 4.3.2 and qualitative responses (Section 4.3.3) in different treatment groups.
4.3.1
Accuracy Difference
We find statistically significant difference in the mean accuracy of all treatment groups compared to the control and reject the null hypotheses H1 through H4 in Section 4.2
Participants using SHAP perform significantly worse than the control (p 0.001599 0.05).
Participants using BERTSum perform significantly worse than the control (p 0.0056 0.05).
Participants using Co-occurrence method perform significantly better than the control (p 0.002997 < 0.05).
Participants using using the Semantic method perform significantly better than the control (p = 0.002997 < 0.05).
oWe do not include statistical tests for the easy questions because they not part of the pre-registered experiment, This for two reasons. First, if the for the easy questions much larger draw reliable conclusions about the methods (taking multiple testing into account), increasing the of the experiment significantly. Second, we were more interested in the methods ability assist with the hard questions such situation is likely for additional assistive information. Hence, prioritized testing the hard questions.
be included, the effective sample size would have become
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Average Accuracy on Questions
Average Time Spent on Questions
1.0
hard
Time question (sec)
50
0.8
easy
40
Accuracy
30
20
model acc. (hard)
model acc. (easy)
0o o
hard
Control
SHAP
BERTSum
Co-occurrence
Semantic
easy
Control
SHAP
BERTSum
Co-occurrence
Semantic
(a) Average accuracy with 95% confidence intervals.
(b) Average time with 95% confidence intervals.
Figure 6: (a) On hard questions, we observe significantly higher accuracy in groups presented with assistive information from Co-occurrence and Semantic methods compared to the control, and lower accuracy in groups presented with ussistive information from SHAP and BERTSum. The dotted lines indicate the accuracy of the matchhng model, i.., accuracy when selecting the article with the highest affinity score. For easy questions, it is more effective to simply follow the affinity scores without support from additional assistive information. For hard questions where the correct match is less obvious, using Co-occurrence or Semantic methods may be effective. (b) We observe lower average time for groups given SHAP, Co-occurrence, and Semantic methods compared to control in both types of questions, but higher average time for BERTSum.
Comparing the participants' accuracy against the model accuracy on different question types, we verify that the assistive information is particularly useful for the hard questions (Figure 6ab. Note that the model is only accurate around 33.3% of the time in hard questions (red dotted line). The information from Semantic method is the most effective for the hard questions with the highest average accuracy of 58.6%, which is 26% increase in accuracy compared to the control (46.6%) and 77% increase compared to the model accuracy (33.3%), while SHAP (35.2%) and BERTSum (36.7%) remain less effective (red checker-patter patterned bars).
On the other hand, there appears no significant difference in accuracy among the methods on the easy questions (blue dotted bars), all of them slightly less accurate than the model accuracy (blue dotted line). Highty-overlapping confidence intervals among all methods and the control also imply that none of the methods tested may be particularly useful on the easy questions compared to the control. The diverging behaviors of the same methods in easy and hard questions suggest different regimes in which the methods be particularly effective: while it is more efficient to simply rely on the affinity scores for the easy questions, assistive information (via Semantic method) can be particularly helpful for the hard questions, when the correct match is less obvious for both the models and humans. This further suggests that for the best results in practice, it may be useful to consider first identifying the difficulty of the question and then dociding if additional information should be provided.
4.3.2 Time Difference
We record the average response time (in reconds/question for participants in each treatment group. We observe that on average the participants using SHAP, Co-occurrence, and Semantic methods respond more quickly compared to the control group for both types of questions (Figure 6b). For both casy and hard questions, the participants using the Semantic method take the shortest average time (26.6 seconds for easy and 32.9 seconds for hard), which is approximately a 20% improvement over the control (34.4 seconds for casy and 41.7 soconds for hard). The participants using BERTSum take the longest (36 soconds for easy and 48.9 seconds for hard), where they experience 17% increase in time for the hard questions. Given that the Semantic method is also able to significantly boost the task accuracy, it is the most effective method among
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the tested ones. On the other hand, BERTSum simultancousty decreases the task accuracy and increases the completion time, it may be considered the least effective method.
4.3.3 Qualitative Responses
Were the highlights helpful?
Strongly agree Agree Neutral
Disagree / Strongly disagree
SHAP
BERTSum
Co-occurrence
Semantic
62.3
83.6
87.0
94.1
3.8
3.6 12.7
5.9
5.6
13.2
20.8
Figure 7: Participants' responses (in percentage) for "Were the highlights helpful?". For all the methods, the majority of parlicipants respond posilively to the question regardless of their actual task accuracy.
At the end of the user study, the participants are asked several qualitative questions about the task.
"Were the highlights helpful?" Participants from all of the treatment groups generally respond positively to this question Figure 7 shows the proportion of different responses from the participants in each group, and positive responses in blue form the majority in all groups. While the participants believe the highlights to be helpful, their task performance shows the contrary for participants using SHAP and BERTSum. Such discrepancy between the subjective perception a tool's utility and the objective utility measured by task-grounded performance metrics corroborate similar previous observations on different assistive tools (Kaur ct al. 2020 Bansal et al. 2021
"What information was the most helpful in answering the question?" While the majority of the participants using BERTSum, Co-occurrence, and Semantic methods respond that the highlights were the most helpful, the participants using SHAP have more diverse responses that showed no particular preference (Figure It is interesting to note that the participants using either Co-occurrence or Semantic methods find the role of highlights to be significantly helpful when compared to other methods.
What information was the most helpful in answering the question?
Control
SHAP
BERTSum
Co-occurrence
Semantic
Highlights
77.6
37.7
65.5
82.4
72.2
Text itself
Score
1.7
26.4
3.8
3.6
3.9
Others
19.0
11.7
32.1
21.8
9.1
13.7
3.0
1.9
None
Figure 8: Participants' responses (in percentage) to question "What information was the most helpful?"
"Were there loo mony highlights?" We find that the participants using SHAP most strongly agree to this sentiment (Figure 9). One factor that could have contributed to this is the default output values from SHAP used generate the highlights, which are not post-processed for more succinct representation of information. Appropriate post-pro sest-prccessng of the attribution scores may be necessary to better account for this the impact of the amount of highlights the task performance open research question that requires future work.
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Were there too many highlights?
Strongly agree/ Agree Neutral
Disagree / Strongly disagree
None
SHAP
BERTSum
Co cc-oocurreece
Semantic
47.2
27.3
18.2
27.5
23.5
33.3
22.2
5.6
28.3
20.8
54.5
49.0
38.9
Figure 9: Participants' responses (in percentage) to a question "Were there too many highlights?"
5
Conclusion
Motivated by practical concerns in document matching tasks with human decision makers, conducted user study to investigate the utility of different kinds of assistive information for the nummary-article matching task. We found that even well-established black-box model explanations can potentially impair the users' decisions, while task-specific approaches can effectively assist them. Existing methods are typically not explicitly optimized for the task's objective: Model explanations are contingent on the matching model; it altempts to explain what the model considers important, not. necessarily what human users find important lo perform well in the task. This misalignment can be particularly confusing to the users if the model prediction is wrong (c.g., observed in hard questions of the summary-article matching) where the explanations are more of "justification" of why the prediction is wrong. General lext summarization methods can be helpful for succinctly expressing a high-level topic of the article, but may lack the precision of picking the details directly related to the given summary. Furthermore, we observed that the users' subjective perception on the utility of (assistive) information was misaligned with their performance on the task. These results altogether emphasize that is important for the developers of such assistive tools to articulate the specific use (and users) it serves, and rigorously evaluate their proposals.
We believe that the summary-article matching task can be used first- past-pass test to validate promising methods (and promote development of new approaches). Relaxing some assumptions in our setup can provide further insights on strengths and weaknesses of individual methods in more complex scenarios (c.g., allowing multiple or no ground-truths in questions). Complex and lengthy docunents in practice (e.g., academic papers and reviewer profiles) may require longer data collection phase and more scalable adaptations of the presented methods. For instance, visualizing all highlights with different colors at once (as we did in the summary-article matching (ask) may not be practical for longer documents. Necessary changes in the user interface can address such issues, e.g., the highlights can be shown interactively based what information the user is interested in. We present preliminary details of how adapt this too for peer review use case in Appendix E with some initial feedback. It could also be valuable to explore ways to improve the affinity scoring model itself, potentially utilizing the extracted information presented by these methods.
Broader Impact Statement
The assistive methods should be subject to further experiments before being applied to domains with major social implications, e.g., hiring. The methods should also be designed and revised to abide by any regulatory guidelines within. The methods' output also should not serve as the sole justification for confirming a match in practice, as is only intended as a supplementary information that the users may use at their discretion. The correctness of the output of the method is not guarantoed and can vary deponding on the internal models used, which require further independent. research and experimentation.
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Acknowledgments
We thank Kasun Amarasinghe, Siddhant Arora, Keegan Harris, Nari Johnson, and Yilun Zhou for helpful feedback and discussions. This research was approved by CMU Institutional Review Board (TRB). This work was supported in part by the National Science Foundation grants CIF11421224 I1S1705121, IIS1838017, IIS2046613, IIS2112471, CIF1763734 and funding from Meta, Morgan Stanley, Amazon, and Google. Any opinions, findings and conclusions or recommendations expressed in this material are those the author(s) and do not nocessarily reflect the views of any of these funding agencies.
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Terne Thorn Jakobsen and Anna Rogers. What factors should paper-reviewer assignments rely on? community perspectives on issucs and idcals in conference peer-review. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguisties: Human Language Technologies, pp. 4810-4823, Seattle, United States, July 2022. Association for Computational Linguistics. doi: 10.18653/v http///122222meee//// www.l-aminnn3n.... URL httppe///aclaathhooog... http://oor/222..mmmmmmmaaaa.aaa naacl-main .54,
H. D. Tran, G. Cabanac, and G. Hubert. Expert suggestion for conference program committees. In 2017 11th International Conference on Research Challenges in Information Science (RCIS), pp. 221-232, May 2017. doi: 10.1109/ N0.110/RRCS..0 2017.796544.
Xinru Wang and Ming Yin. Are explanations helpful? a comparative study the effects of explanations in Al-assisted bocision-making In 26th International Conference on Intelligent User Interfaces, pp. 318-328, 2021.
John Wieting, Kevin Gimpel, Graham Neubig, and Taylor Berg-Kirkpatrick Simple and effective paraphrastic similarity from parallel translations. In ACL, pp. 4602-4608, Florence, Italy, July 2019.
Fangzhao Wu, Ying Qiao, Jiun-Hung Chen, Chuhan WU, Tao Qi, Jianxun Lian, Danyang Liu, Xing Xic, Jianfeng Gao, Winnie Wu, and Ming Zhou. MIND: A large-scale dataset for news recommendation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3597-3606, Online, July 2020. Association for Computational Linguistics. doi: acl attps://aclanthology org/ 2020 acl-main 331
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Jingqing Zhang, Yao Zhao, Mohammad Salch, and Peter Liu. Pegasus: Pre-training with extracted gap- sentences for abstractive summarization. In International Conference on Machine Learning, pp. 11328-1111111 11339. PMLR, 2020.
Ming Zhong, Pengfei Liu, Yiran Chen, Danqing Wang, Xipeng Qiu, and Xuanjing Huang. Extractive summarization text matching. In Proceedings of the 58th Annual Meeting of the Association for Compulalional Linguistics, pp. 6197 6208, Online, July 2020. Association for Computational Linguistics. acl -main.ann....
doi: 1...8653/v1/2220ae main 552. URL https//belaarchooogy.orr
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Published in Transactions on Machine Learning Research (07 (07/2023)
Appendices
A Proxy Test of Black-box Model Explanations
There several black-box model explanations to consider for the task. While testing all of them real users be an interesting research its own, information could be helpful, we decide to select one representative method in the literature. As there is no absolute answer to which method is superior, we conduct simple proxy test of what method can be a better choice for the task.
more broadly interested in what distinct types of
We consider the following feature attribution methods: Integrated Gradients (Sundraraaaa al, 2017 p
Input Gradients (Shrikumar et al. 20161, and SHAP (Lundberg & Lee 2017). In Figure 10 we plot the mean EM distance (averaged across 50 different random attributions, normalized to be between 0 and 1) between the distribution of attribution scores for the input tokens in ground-truth articles. The higher the value (darker the color), the more distinct the distribution of the attribution scores computed by respective methods. Notice that SHAP shows the most distinct distribution from random attributions compared to other methods, indicating may be a better choice that carry more information about the important tokens. We have also qualitatively verified that the highlights from other two methods were not as meaningful as SHAP on the articles.
Note also that SHAP is promising candidate to apply to the task due to its popularity and its common presence in more sophisticated domains like biology, physics, chemistry, and finance (Jesus et. al. 2021
Novakovsky et al.) 2022 Yang et al. 2022 Zablocki et al. 2022 Pueci et al. 2022).
Normalized Average EM Distance
int_grad
0.00
0.00
0.00
0.00
input_gras
0.67
0.00
0.00
0.00
shap
0.58
1.00
0.00
0.00
random
0.64
0.64
0.82
0.00
int_ grad
input_ grad
shap
rendom
methods
Figure 10: Proxy quality test for the black-box model explanations using average EM distances between
the distributions of attribution scores of input tokens. The higher the values (the darker the color), the more different the distribution of the attribution scores. SHAP shows the most distinct distribution from the random attributions (bottom row).
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Published in Transactions on Machine Learning Research (07 (023)
B Method Details
B.1 SHAP
We use the native implementation of the method (https://github. httpa//..oo/mllddbbrsssaa//a/ version 0.40.0, and use the default hyperparameters set by the library. We apply SHAP to the malching model, which takes in the summary text and one candidate article to compute the affinity score. The matching model uses the embeddings from DistilBART (https: hettps:/hhuggingface wo//ssile..fr//astiibrrr cnn- 12-6) fine-tuned on the same CNN/Daily Mail dataset, and compules the cosine similarity between the embeddings.
B.2 BertSUM
We use the native implementation of the method (https://github. directly to the candidate articles. In particular, we use the extractive model in the repository that has been finc-tuned on the same the original source code, extracting three sentences for the summary.
hattor//ggihhuu..mm//agagr//t// how/://pyyaggg//eeSuuma and apply it
dataset (BertSUMExt). We use the default hyperparameters set by
B.3 Co-occurrence Method
Then use the Python package library to first tokenize the candidate articles into different sentences. to compute the sentence-wise ROUGE-L Fl scores. These scores are used to highlight the sentences. We assign different colors of highlights to parts that are relevant to the different sentences in the summary.
We NLTK (LLK(ittpp::/www.ntt...co
B.4 Semantic Method
Similar to the co-occurrence method, first tokenize the candidate articles into sentences. Then we use teanrtaceeettaassfoooreessstttpp
html) library version 2.2.2 (model used: all-Mini1M-16-v2) to obtain sentence embeddings, which are subsequently used to compute the sentence- level affinity scores. These scores are then used to highlight the sentences. The highlighting scheme remains the same as the co-occurrence method.
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Published in Transactions on Machine Learning Research (07 2203))
C
Method Examples
We show below some example highlights presented to the users using different methods
Summary
The United Nations Relief and Works Agency chief will visit Yarmosks camp Saurdyy Milliant groups are currenlyy in control of the camp. Yarmosk has beon ongallied in fighting since December 2012.
Article 1 -- Scare: 0.75
Article 2 Score: 0.73
Article 3 -.- Score: 0.72
(NN) The commissioner - general of the United Nations
CINN Thousands of Palestinians are trapped in the devastated
(CNN) They took Yarmouk by storm, a sea masked men fooding into the street of one the world's most beleaguered places. Besieged bombal by Syrian forces for more two years, the desperate residents of this Palestinian refugee
Relief and Works Agency will make an energency visit to the
Yarmouk refagee camp in Syria, which has moetly been seized
Yiemouk Palestinian refugee camp in Syria Satunday,a
by sroups including activists repor. London- Syrian Observatory for Human Rights says ISIS and the al Queda aftiliated Al Nesra Front took control of 90% of the camp southern Damoscus Calling lives Yarmouk refugees fprotionnlyy thrratttedd 'on Sunday, the United
spokesman says. Commissioner General Fierre Krahmbhth
will cosess the hamanitarian situation in the camp and speak
camp near Dumascus awoke in early April new, even more
individuals about ways
suffering the
tamifying ruality ISIS miliinnts seicing Yammou
people who remain there." Thhe visit prompted by LINKWA's
derienting several militia groups operating in the area. area.//hey Thee slaughtesed them in the steetss" 2 one Kammoukresident, who pakod not namod, told CNN. "Thy caught) people
deepening concern for the safety and protection of 10,000
Nations Relief and Works Agency issued a statement uring
Palestinians and Syrian civiliaes, including 3,500 children,"
humaniterian acuces. NNever
more
agency spokesman Christopher Gunness told CNNss Paula
desperase the Palestine refugee camp of Narmoukuhthh sutement said, The UNRWA estimanes 15, 000 civilians
and killed them in the street, in front of people. The Islamic
Newton. YYarmouk remaine under the control of armed groups.
State is contoll of almost all the cump." An estimated
civilian continues to threatonod by
the
somain trapped the camp has engolied fighting between the govermment and rebel forces since December
18, 000 refugees are now trapped inside Yanmouk,
confict." Krthenbuhi will meet with senior Syrian officials, U.
between ISLS and Syrian regime forces in 'the deepest circle
N. and relief agency staff members, and displaced people from
2012. Syria's state run SANA news agency reports up 2, in past two days food, water medical supplies remain scarce. 'All people are trying to leave
hell," in the words of U.N Secretary- General Bun Ki moon.
camp
Yarmouk rofugee camp, which sits just
people
Yarmouka the largest Palestinian refugee camp Syriauwas formed in 1957 to accommodate people fleeing the Arab
miles from central Damascus, has been enguifed in fighting
hetween the Syrian government and armed groups since
the camp." says Syrian activist Abu Moharamed in Damiscus
Seneli conflien, The camp, which sits just 6 miles from central
December 2012: London- based Syrian Obscevatory Human Rights says the militant group ISIS and the al Qaeda affiliated Al Nusm Front control ahout 90% of the comp. The Syrian government
live Yarmouka! Thore eloczicity! says
Damaseas, las engullfed fighting between Syrian govermeent and amedd groups since December 2012. London- based Syrius Observatory for Human Rights
Mohammed. "ISIS commos the horpital so injured people have howhere go." The Syrinn Ohservatory for Human Rights exports bacnel bombs were dropped on camp Sunday as lashes continued. The Palestine Liheration Organizarion called on international bodies to assist in the evacustion of people the camp. 'Reports kicnappings, behewdings and mass killings are coming nut from Al Yarmuuk, which is inder a hemi compsign of murder and cccupation. Paloxine Liberation Oxganization
orgamication claims
and Qaeda- affilinted Nuara Front control about 90% of the camp. The organization also claims that the Syrian govemment has dropped harrel hombs on the camp in out
dropped harrel hombs on the camp as recently as Sunday in an
effort drive out armed groups. Yarmouk was fomeed is 1957 Iscacli conflict and is the largest. Palestinian refugre camp Syria. The U.N
accommodate people displaced by the
cctups.Activiits Activists and residents in Yarmouk tell CNN that as many as 1. 000 people have tried to fhe their heenes since ISIS stormed the camp. but have no place to go. Handrods have boen injured, but camp's only functioning hospital was first occupied by ISIS, then targeted fre week regime shelling. As the fighting raged in
on affert
relief agency estimates that there were 160, 000 people the camp when conflict began in 2011 between forves loyal
Committoe Member Dr. Sach Erekat said Saturday. Yermous, the largest Palestinian refagge camp Syris, was formed in 1957 to accommedise people floeing Ana lscadli conflict.
President Bashar al Assad and opposition fighters. That
number has dropped to shout 18, 000, according to estimutes.
Yiemouk been largoly off aid since November
Yarmook, the director of Juira Foundation group that has been able to get into the camp- puinted a grim portrait of the conditions on the ground since ISIS arrived. "We nced modicine accoss to trcotment and medical facilities"
aid
2013, There have been widespread reports of malnutrition and shortages medical care. "We will not alundon hope." !
humanity that we have seen have now descended into further evels of inhumanity, said Cheis Gunness, spokesman for the
Ounnoss said. will suhmit possimism, because to
UNRWAA Yamook, added, always a place whece human rights meant very little. We are seeing descend Nurthrr! CNN's Sumim Said contriuted to this report.
ahandon hope would he in ahandom the people of Yarmouk....
Wesarn Sahunch told CNN, "The last hoepital in Yarmouk
We canner shandos the people of Yarmook, and we will not. hence mission!!
camp was hombed yesterday, there's really nothing
fractioning Opinion: Save the 'miracle babies Even
delivering clean water in Yarmouk can he deadly task. Majed
Alomari, the Infra Foundation's water coordinator, wns killed
few days ago gunned dows an 1SUS firefight with rehel groups. The head of the Palestinian League for Human
Rights in Syria
Figure 11: Example highlights for SHAP.
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Published in Transactions on Machine Learning Research (07/202) 2023)
Summary
The United Nations Rolief and Works Agency chief vist Yarmionk camp Samuday. Milltaat groups see cunently cestrol ctmp. Yammook has been angulleed in fighting since Docember 2012.
Article -.. Score: 0.75
Article 2 -.. Score: 0.73
Artide 3 ... Score: 0.72
Chh commissionn general of United Nations Relief
Chomnhomcnnc Palestininss aee trapped devistated
(CNN))bey took Yormook by storm, a sea masked mon Bloding into the streets of one the world's most beleaguered places. Resieged and homhhad by Syrian forces for more than two years, desperate residents
and Works mill make an emergency wisit to the Yammouk Palestinian refugee camp in Syrin Satundayua
Yarmonk refigeee cump in Sycie, which has mostly been selzed
by groupe including ISIS, activists report The Eorslon-bsadd
spokeemon soys: Comminsioneneennall theme Krthenbahl
Syninm Olbveraaoory Human Rights says ISys 1SI5
Palestinian refugee
will mosess the huroanttariu siturtoon in the camp and speak
Quada affiiiittedt Aulit Front took control of Mtatis of the
camp near Dumascus awoke in early April a new, even mare terrtyynng reality ISIS milliants seiving Yarmouk after dedenzing several militia groupe operating in the seea, "They slaughtored them in the streets, one Yamnuu resident, who osked not named, told CNN. "They (conght) throe people ond killed thee in street, in frout of people. The Islamic
with individuil about to relieve the suffering ofe
camp southern Dumascus Galling the lives Yarmouk
people who remain therea" TThe
peompted by UNRWA's
cofuzees 'profocudlly threatmsed ou Sunday, the United
deepening concern for the safety and protection of 18,0000
Nations Retse and Mrtuks Agancy issued a statement urging
Palestinians and Syrian civiliaes, including 3, 3,500 children," ?
humanitseiinn aid arzonas "Never har the hour been mon:
agency spoknsman Christopher Gunness told CNN's
desperate the Italestine refugee camp Yarzook,"" statement said. The UNRWA estimutes 18/000 civilians remsii rappeal in the camp (hat has hoon engulfedd in fighting botwarn government and forces since December 2012. Syynials
Newton. "Yarmnuk remains under the control of armed grnupe,
State is mise control of almost all the cumpatii extimated
and civilian life continues to be threstened by the effects of the condict.. Krihbenbhhl meet senior Syriam officials,
18 noD refugres are now trapred inside Yarmouk, stack betwoen. and Syrian rogime
in "the deepest circle
U.N. and relief agency staff members, and displaced people
statemtun SrAN news agancy reporis 2,000 people
" in the wonds of U.N. Secretary General Ban Ki-moon.
from the camp iteeff The Yammork refugee whichsiis
Red in the twr days ao food water and medical supplies
Yermonk the lorgest Polestinion refugen camp in Syninn
miles from central Dumascus, has been engulfied in nighning hetween the Syrian government and armed grmaps
scances' people are cying leave the cump.' says
formned 1952 to accommodate people deeing Aambe lamelli confliena The camp, which sibs just 6 miles from central
Syrian activist Alu Mihhameed in Damascus who used live Yarmouk 'Thore olecricityy" Molammed. "ISIS Damasous, has been engallied fighting betwooe the Syrian government and armed groups since Decomber 2012.
sinoe December 20 I Lemfon-beed Syrian Observatory
Human Rights says the militant group ISIS
controds the hoopital so injured people have nowbere go."
Quoda affiliated Nl-Nusm Front control about 90% of the
The Syrian Obbervvtooy for Human Rights reports harrel bombs were dropped the camp Sunday clashes continued. The Palestine Liberation Organization intemationnl bodies to seeist in the evacuation of people from the camp. 'Reports of kichappings, behenclings and killings are coming out from Yurmou, which is hrural compuign of marder and cecupation, " Pulestine iiberation Organization Exeeutive Committee Member Dr.
London- based Syrian Ohservatary for Human Rights says ISIS and the Queda-afiliited Al-Nusra Front control about 50%
camp. The organization also claims that the Syrimn govemnent drapped barrel bombs on the camp as recently Sunday an effort to drive out ammed groups. Yamouk wus fomsed in 1957 to accommodate people displaced the Arab-Lameli confiict is the lurgest Pulestinian refugee camp in Syria.
on
the camp. organization also claims the Syrian
govemment has dropped herrel hombs on the camp in an effiet
drive remed groups. aeetivists and residents in Yarmouk tall CNN that many un 5:000 people have tried flee their
The U.N. relief agrncy extimates that there were p60
homes since 1885 stommed the camps hus have no place to gm
people the camp when the conflict began 2011 between
Hundreds have injured, the camp's only functioning
furees lnyal to President Bashar al-Assad and opposition
Saeb Erekat said Saturday. Yurmuk, the largest Palestinian refugee camp Syris, was formed in 1957 to accommeduse people deeing the Arab Lsmeli confict. "The levols of humanity that we have seen now descended into further evels of inhimanity, " said Chris Gunness, spokesmon for the UNKWA. Yammouk added, "was always a place whece human rights meant very little. We are seeing descend Auther: CNN's Somin Said contribtte to this report
horpical was first occupied by ISIS, then targoted last week by regime shalling As the fighting raged in Yarmeuk, the director of the Jufen Foundation the only aid group that has been able get into the camp - paimed a grim portrait the conditions on the ground since 1SIS arrived, "We neaa medicine and to tratment and medical facilities, 1 Wessum Sabaneh uold CNN. "The last hospital in Yarmouk camp was humbed yesterday, so them's really nothing fimctioning Opinion: Save the 'mincle bobses Even delivering water Yarmouk
fighters That number has dropped to about 18,000, acconling estimates. Yormouk has been largely off from aid November 2013. There have been w widespread reports of molnaaritinn and shortagos of medical como, "We will not abandon hope," Gunness städ. "We will not submit pessimism, bocause to abundon hope would tn ahandon the people of Yermouk.... We cannor shandon the people of Yarmouk, and will not, heace this mission."
can he deadly taik. Majed Alomari, the Jufra Foundation's water coondimter, wis killed a few days
gunned down
LSUS fredight with rival rebel groups. The head of Palestinian League for Human Rights in Syria (PLHR), who
ted the camp and Syria in October 2012, said the people of Yarmouk were dire need of help.
Figure 12: Example highlights for BERTSum.
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Published in Transactions on Machine Learning Research (07 (07/202) 2022)
Summary
The United Nations Rolief and Works Agency chief vist Yarmionk camp Samuday.
Millitan groups see cunently cnstrol the ctmp. Yammork has been engulleed in fighting since Docember 2012.
Article - Seore: 0.75
Article 2 - Score: 0.73
Articl 3 - Score: 0.72
(CNN) y commissioner general the Limited Nations
CNN > housannds Palestinians are trapped in the devoetatte Yirmmdsserages camp in Syrie, which has mostly boen seized by groups including ISIS astivists report. The London based Syrian Obsersatory for Human Rights says ISSS and the Quoia- affilitta Nisak Frour look control
CNN y They took Yarmouk by stoem. sea masked men Bonding into the streets of ore the world is most beleaguereed places Besieged and bombed by Syrian fonves more tham two years, the desperate residents ths Paleszinian refugee camp ncar Dumnecus awokc in early April a new oven ying reality - ISIS miliiani seicing Yarmouk allur dedeacing several militia groupe operating in the area They slsughtooed tham in the stecets ," oon Yarnook resident, who paked not named. told CNN. : They ( (caugh) three the street, front of people. The Isdamnic State is now in contomtttallmeee all the camp Al cosimated 18 18000 refugues
Moliofannt Warker Agency wilk an emergancy sisit the
Yormonk Pallosiinim rofugoe samprin Syria Satumiy
spokesman says. Commissioner- General Piere Krthhenbah will moses the Iummanitaiin ias situstion in the camp and spask
with individuals about
relieve the sulfering the visit is prompted by UNKWA
50 Scoof the sampain suncherm Dumascus Calling the lives
people who remain there..
tramoon refugges prodormdlyy threatened on Sunday:;
coeppning concom for the salidy and protecionn of 18,000
tae Unitedd Nations Melist and Works Agmaeygsgmmy
aalessiiians and Syriam civilians, including 3,500 children.
stattorent urging humamitarion and
Never han hour been more desperate in the Palestine refagee camp of
agency spolessman Christopher Gunness told CNN 's Fazla
people and killed
Newton. Yarnoak remsins under the contotntiamned
You mosk. tll statement ssid. The UNRWA estimates 18,000 civiliians remain trapped the camp that has been engulfesd nghning betwees the government and rebel forces since
gnoups and civilian life cumtiness be threatened the chestcittict. Kriitenbhhl meet senior
trapped inside Yarmouks stuck between ISIS Syran regine fonces
deepest
Syrisn officials, U.N. and relief sgancy staff meubes, and displaced people from the camp itself. The Yarmuuk refuges camp . which sits just sngssflgl in fighting betwoen the Syrimm government and armedd groups since December 2012: The London - bused
Dccambeer 2012 Syris' state - rua SANA news agency ropurs 2 peuple have fled the past days food. water and medical supplies remain acarce. All people
carke of Itll in the words of U Secustayy = GGenmal Ban Ki maon mww.mmammmmmaaaae Yarmouk. the lorgest Palestinian refugee camp Syria, was formed 1957 to accommodtte people fleeing the Amsb Isradi cunflict The camp . which sits just miles from cencaal Dumascus, has been engulfedd in fighting betw the Syrian govemment and samed groups since December 2: The London = based Syrian Obsurvatory for Human Rights ISIS and the affiliate Al Nusra Front control shout S of the The organization claims that
from central Damascus, has
are tyynng to leave the camp. "says Syrimn activist Abua
Mihhammed Dumuseus who used live Yarmouk.
Syyrian Ohservatory Human Rights says milimnt group
electricity. "says Mohammed ISIS connols the
ISBS and the Queds- suuds-atie affilistted Al- Nusen Front contol
hospital so injured people have nowhere to go. "The Syrian Observatory for Human Rights reports barzel bombs drogped on the comp Sunday clashes coutinued. The
about 90 Se chethaa.mmp.. The organization also claims that
Syrian government has drapped burre bombs on the camp as
eeoenty as Sunday in an effort drive out armed grougs.
Palestine Liberation Organization called on interssional bodics assist the evacuation people from the camp." Repocts of kidnappings, beheadings mose killings are coming out from Al -iemmun which is under a brutal campaign of munder and accupation. " Palestine Liberation Orgunization Executive Committe Member Sach Erekor and Satunday Yarmouk the langat Palestinian refugee camp Syria, formed 1957 accommodate people flecing th Arab- Esmeli conflies. "The levels humanity that we have soen have now descended into further levels of Inhumanity.. '" said Caris Gunness spokesman the UNRWA, Yarmouk, hee added, was always place where human rights moant very litk. We are soeing it descand further. CNN' 's Samira Said contributed this repoct.
Syrian government has dropped bserel bombs on the camp in effort drive armed groups Activists and residents Yarmouk CNN that as many as 5,000 people have tried to flo their hornes since ISES stormed the camp but have no place go. Hundreds have been injured. but the camp only functioning hospital was first occupied by 1815, then targeted hot work by regime sholling. As the fighting ragedl in Yarmouk. the director of the Jufra Foundation the only aid group that been to get into the camp - painted a grim portrait of the cunditions on the ground since ISIS arrivod We need medicine and access treatment and medical faciliiies "Woaa Subanch
Yurmouk was formed in 1957 to accommodate people displaced by the Arab lanaeli conflict and the largest
Palestinion refugee camp in Syria. The U.N. reliof
estimates that there were 160,000 people the cump when the contiict begum in 2011 between forces loyal President Bashar a Assad and opposition fighters i That number has
drogped about ,0000 according extimates. Marmouk has been lurgely out off from since November 2013.. have boen widkspread reports of malnutritin and shortages of medical care. We will abandon hope. Gunness said.
not submit to pessimissms because to abandon hope would be to alsandon the people of Yarmouk un We not abandon the people of Yarmouk yand we wolllnot this
11 The hospital
Yormookc bombed yestorday so there's is really
nothing functioning Opinion Save the 'miracle babies'
Even delivering clean water Yarmouk can be a deadly tuck.
Majed Alomari, the Jafi Foundation 's water coordinstor.
killed few days ago gunned down ISIS firefight with rival rebel groups. The head of Palestinian League for Iummn Right in Syria (PLHR) ) who fled the camp and Syria October 2012, said people Yarmouk were dire neod of help
Figure 13: Example highlights for Co-occurrence method.
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Published in Transactions on Machine Learning Research (07 (07/202) 2023)
Summary
The United Nations Rolief and Works Agency chief vist Yarmionk camp Samuday. Millitan groups see cunently cnstrol the ctmp. Yammork has been engulleed in fighting since Docember
Article 1 - Score: 0.75
Article 2 - Score: 0.73
Article 3 --- Score: 0.72
(CNN) y commissioner general the Unmited Nations
CNN > housands Palestinians are trapped in the
CNN j They took Yarmouk by stoem. sco masked men looding into the streets of one the world is most beleaguened places Besieged and bombed by Syrian forces more than two years, the despenate residents this Palestinian refugee camp near Damascus aweks in early April to new oven serniying reality -n ISIS militants seizing Yarmonks after dedeacing several militia groupe operating in the seea They aloughtored thom in the stoveia ," une Yamnook resident, who paked not named, tald CNN : They ( (caught three people and killed them the street, front of people. The Isdamic State is now in coonttoooflammmme all theremmpull An estimated 18,000 refugees
Matiofand Works Aggnea will make an emergunecy visit to the Yurmok Palestinian refugee campin Syria Satundapuua spokesman says... Commissioner General Piere Ketlkenbahl
devastated Yarmouk reciagee camp Syris, which has mostly been seized by groups including ISIS activitts report. The London based Syrian Obsersatary for Humua Rights says SSSS and the Quida Saudla-aiiliatt afiilittal Niara From look conteal
will sosess the humanitaria situation in the camp and spesk
with individuals about ways relieve the suffering the visit is prompted by UNKWA doopening cuncem for the safoty and protection of 18,000
50 ottshe compain southerm Damascus Calling lives
people who remain there.
of Yumookcrefugees prodoradly threatened on Sunday's the United Nations Rolisf and Workss Agmeegisiss
"alestinians and Syrian civilians, including 3,500 children.
statement urging humanitarian aid accessu"" Never has the hour been more desperate in the Palestine refages camp of Yarmouk. the ststement said. The UNRWA estimates 18,000 civilians remain trapped the sumputhr has engulfed ghting between the gevernment and rehel forees since
agency spokessan Christopher Gunness told CNN's 's Paula
Newton. Ysrmoak remains under the cuntrol of armedd
groups and civilian life continues threatened the the conflict. Kriihennhhl will meet sesior Syriim officials, U.N. and rolief agancy staff members, and displaced people from camp itself. The Yermmnreffagee campi which sits just fo miles from contral Damuecus has been enguifeed in fightnn between the Syrian govermenn and armed groups since December 2012 /hh London: based
trapped inside Yarmouk. dospest cardee of hell. i in the works of U Secrearr- General Ban Ki Yarmonke. the largest Palestinian refugee campiin Syria, was formed 1957 to accommodate people flaxing the Amb Israedi conflict. The comp , which siss just miles from central Domaszus, has eagulfed in fighting hetween
stuck betwees ISIS Syrian regimsc forces
December 2012 Syria state - run SANA news agecc reports up 2,000 people have ried the past days
Good, water and medical suplics renain actsce. 11 All people
crv urying leave thassaps. 1 bays Syrimm activint Abu Mohammed Damaseus who used live Yarmouk.
Syrinm Observatory Human Rights says militant group
There no ckustricity. : "says Molummall ISIS oustrols the
Syriau govermment and ammed groups since Desembhr 2012.
ISES and the al Queda alliiiited Al Niusra Frunt cuntrel
huspital so injured people have nowhore to go. " "The Syriam Observatory for Human Rights reports barzel bombs drogped on the camp Sunday is clashas coutimuol. The
The London - based Syrian Obsurvatory for lluman Rights says LSUS and the Queda-afhliited Al: Nusra Front control about 90 f of campu The organizationn also claims that the Syrian government has dropped barnel bombs un the camp in effort drive armed groups Activists and residenes Yarmouk tell CNN that as many as 5,000 people have tried to flee their homns since ISIS stormed the camp but have no place go. Hundreds have injure, but camp only fonctioning hospital was finst occupied by ISIS, then targoted last week regime shelling. As the fighting raged
abour Se of camps. The organization claims
Syriim government his deupped bunel bontw on tharamgras
revently Sunday in an elfor to drive out armed groupe.
Talestine Liberniion Organization called un inserssional the evacuation people from camp. 10 Repoets of kidnappings, beheadings and moss killings seu coming out from Al Yirmonk, which is under a brutal campaign munder und occupation. Palestine Liberation Organization Executive Commitoo Member Dr. Such Erekon said Satunday Virmonie, the largest Palestinian zefugee camp Syria. formed 1957 accommodare people flacing be Amab Ismeli conflist." x The levels bummnity that we have neen have now descended intn further levels of ithumanity. said Clris Gunness, spokesnan the UNRWA Yirmouk he added, 1" was róways a place where human rights meant very little We are seeing it descend further CCN'' Samira contributad this report.
Yurmouk was formed 1957 accommodate people
bodies
displiccd by the Arab- Isnaeli conflict and the lirgeat
Palestinian refugee camp in Syria. The U.N. relief agency
estimates that there were 160 people the comp when the comflict begam in 2011 betweem foroos loyal President Bashar al- Assad and opposition fighters. That number has drogped about 18,000, according estimates. Yammuu has boen largely cut off from aid since Novenber 2013. . There have been widespread reports of malnutition and shortages of medicaal care. We will abandon hope. Gannness said. We will not submit to prssimism. because to abendon hope
Yarmouks. director the Jufna Foundation only old group that has baen able to get into the samp-- painted a grim portrait of the conditions on the ground since ISIS arrived and medical
We need medicine and facilitios . WWesam Saberch tood CNN . The last hospital
worlld be to abandon the people of Yarmouk... ... We can not ahandon the people of Yarmouk, and will henoe
Yarmouk camp was hombed yesterday ,so there's 's really
nothing functioning 'Opinion :aave the 'miracle hahics'
mission.
Even delivering clem water Yarmoek can be a deadly lask.
Majed. Alamari, the Jarn Foundation is water coorclnaatr..
killad fow days agu- gumned down with rival robal groups The head of the Palestinio Lengue for Human Rights in Syria (LHH)) ). who ried the camp and Syria October said people Yarmosk were dirc nced of help.
fiedlight
Figure 14: Example highlights for Semantic method.
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Published in Transactions on Machine Learning Research (07 (07/202) 2023)
D User Study Details
D.1 Pilots and Sample Size
Prior to conducting the actual user study, ran pilot studies smaller number of parlicipants. Using the data points collected from these studies, we conducted Monte Carlo simulation-based power analysis to determine the effective sample size. We determined to recruit 55 participants per condition (so total of 275 = conditions) for a statistical power over 0.8 with the effect size (Cohen's d) of 0.5 (orange line with circle markers Figure 15 This effect size corresponds 0.1 difference the mean accuracy between the control and the treatment.
Power Analysis: alpha=0.05
mcan-diff-0.05 cohensd-o.22
mean-diff=0.Bt co05
mean-dif=0015, cohees---0,75
mean-diff-0.22 cohens-d=1.00
effective sample
Power
0.6
0.4
50
Number of Samples
Figure 15: Power analysis for the effective sample size. We collect 55 samples per group (vertical dotted line) for statistical power over 0.8 for the effect size (Cohen's 's d) of 0.5 (orange line with circle markers).
D.2 Demographic Background
we provide demographic background of the participants (age, ethnicity, student stalus, employment status) recruited for the study. 275 participants were recruited from balanced pool of adult males and females located in the U.S. with minimum approval ratings of 90% using Prolific www.proolffic.co/
In Figure
16
D.3 Tutorial
We provide the participants with set of instructions laying out what the highlights indicate and how might use them for the task. The instruction is followed by two sample questions which the participants could take unlimited time to get understanding of what the questions look like. For the sample questions, the participants were provided the correct answers and the justification behind them as feedback.
D.4 Payments
Base payment per participants was $3.15, determined based the minimum hourly payment set by the platform and the median completion time of all participants, resulting in an average reward of $12.07 per hour. To encourage quicker and more accurale responses, we designed bonus payments so that each participant could earn additional (base payment for the question x multiplier) for each correctly answered questions, where the multiplier determined by the response time on the question (Table D. One could idcally carn up x x1.5 the base payment by answering all questions correctly, all within 30 seconds. All payments (base and bonus) were processed after the data collection complete, accounting for invalid responses.
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Published in Transactions on Machine Learning Research (07 2022)
Age
Ethnicity
than
Mixed
5.4%
4.2%
51 60
6.0%
16.3%
21 30
Black
28.3%
10.1%
Asian
6.0%
41 50
Other
14.5%
1.5%
White 76.5%
31 40
31.3%
Student Status
Employment Status
Yes
Part-Time
18. 18.7%
Full-Time
35.3%
Other
12.6%
to start
1.8%
Not in paid 15.6%
Unemployed
15.0%
Figure 16: Demographic background of the participants (age, ethnicity, student status, and employment status).
Response Time (scconds) <
30
<
60
90
< 120
> 120
Multiplier
x0.5
x0.4
x0.3
x0.2
x0.0
Table 1: Reward multiplier based on response time for correct answers. Incorrect answers have the multiplier
of zero.
E From Summary-Article Matching to Peer Review
In this section, we describe our initial steps toward translating the summary-article matching setup to the peer review setup. We introduce tool that uses the academic abstracts and the semantic method to assist meta-reviewers check the quality of potential match between submitted paper and reviewer.
Based on the results obtained in our user study for summary-article matching, now apply the semantic method to a more specific set of documents for peer review: academic abstracts. Abstracts contain rich and compact information about what the paper is about and are therefore widely utilized in practice. For instance, large language models use the embeddings of the abstracts subsequently assign affinity scores each candidate reviewer (Cohan et al. 2020
Meta-reviewers generally "are presented with little structured information about the reviewers" (Thrn Jakob sen & Rogers 2022): what the reviewesss typically see is just list of candidate reviewers, sorted according to their affinity scores (Figure 17 left). They can then click each reviewer's profile to see more details about the reviewer (e.g., Google scholar page, personal websites, etc.). The tool we develop, which we call R2P2 (Reviewer TO Paper for Peer Review), is designed to provide more structured information about each reviewer relevant the submission. It takes in a title and an abstract of the submitted paper, and a potential reviewer's 's profile (Semantic Scholar profile link). then searches for previous papers written by the reviewer and ranks each paper based on its abstract's similarity to the submitted abstract, computed
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Published in Transactions on Machine Learning Research (07/202) (022)
0.81
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0.79
0.79
(e.g. TPMS,
Openreview)
B
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0.75
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What users typically see
What users can see with R2P2
Key Papera
Relevant Parts
Overview of R2P2. While typical meta-reviewers only see the ranked list of potential reviewers and their respective affinity scores (left), R2P2 helps provide more context to the match, by searching for top relevant papers by the reviewer, well as relevant pieces of information within those papers (right).
Figure 17:
with embeddings from a language model of choice Once the papers are ranked and top papers are retrieved, it further uscs the language model highlight relevant sentences within the abstracts of the retrieved papers (Figure 17 right), just as we have done in the summary-article matching setup. The tool is available for trial here
Unlike the summary-article matching setup where the users are simply asked to process three candidate articles and a short paragraph of summary text, this setup involves longer imput texts (e.g., submitted abstracts) as well as multiple candidate texts (e.g., reviewer's previous papers) to process. This requires a change in how the highlights and relevant information are presented, as visualizing all highlights with different colors once with all text (as we did in the summary-article matching task) not practical. R2P2 displays more stratified set of information as described below.
Initial Information. First, the users are shown succinet characterization of what parts from the abstracts (one from the submitted paper, one from the reviewer's previous paper) make them similar. In particular, we pick the top relevant sentence pairs (based on relevance scores computed by the language model) from the submitted abstract and the abstracts of the reviewer's top papers and display them side-by-side (Figure 18). We further allow users to change the number of lop papers by the reviewer and lop sentence pairs to show for each paper.
Additional Information. If the provided information above is not sufficient, the users have choice to explore each of the reviewer's previous papers in more detail. This interactive part gives the users more degroes of freedom to select specific reviewer's paper, specific sentence from the submitted abstract, and the number of relevant sentences to show from the abstract of the selected reviewer's paper (Figure 19). The red highlights indicate the relevant sentences from the selected paper's abstract, where the darkness of the color scales with the magnitude of the relevance score (the darker the higher). We highlight common phrases contained in both the submitted abstract and the reviewer's paper blue. The users can interactively click different parts understand the similarities and differences between the reviewer and the submission. It. is also useful to look at the list of titles on the left to quickly verify if the reviewer has published similar topic to that of the submission: in Figure 19 WC see that two papers have high affinity scores above 0.92, all of which are about peer review.
Preliminary Feedback. We perform preliminary interviews with few colleagues to collect initial qualitative feedback about the tool. To make them interact with the tool in more meaningful manner (and therefore make them more likely to provide quality leedback), devise small task that emulates the mela-reviewer's job of evaluating the quality of the given paper-reviewer assignment. Specifically, they are asked to use the tool to rate the quality of 5 different paper-reviewer assignments, using score ranging between 1 and 5 (5 being the strongest match, 1 being the weakest). The paper-reviewer assignments are sampled from a
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Published in Transactions on Machine Learning Research (07/ 2202)
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Figure 18: R2P2 first provides relevant sentence pairs from the submission abstract (left) and the top papers by the reviewer (right). Example here is for the TPMS paper (Charlin & Zemel, 2013 as the submission and Tom Goldstein (https: wwtp://wmwtticcchhl..ooooo .wwggoorgooo.. wrgg/uthor/T... -oodds-Gllldeeinn (eetttinn119628888 1962083] as the reviewer. From the presented information, it is easy to catch that both the reviewer and the submission have a common interest in conference review process.
gold-standard dataset, which consists of "self-reported expertise scores provided by researchers who evaluated their expertise in reviewing papers they have read previously" (Stelmakh et al. 2023].
Generally, the feedback was positive, with suggestions for improving the UI and the algorithm. One common feature that the participants found particularly useful was the title of top relevant papers from the reviewer's profile. Part of the common workflow was to go through the list of these titles to deduce how the reviewer may be relevant. It was also pointed out that skimming through the reviewer's publication list manually without such information would make it easy to miss few critical papers that are highly relevant. Additional information with a more interactive UI was also perceived as helpful with the highlights capturing important details for the task.
Sentence-by-see by-sentecee comparison presented for the initial information was generally useful, but the participants suggested it could be improved in terms of selecting the sentences to display. There were cases where the selected sentence pairs were introductory sentences of respective abstracts, which contain less specific information about the assignment. While they were useful in checking for a general alignment of expertise, more detail was needed to ensure the quality of the match, which was more effectively captured in the interactive part of the tool. One suggested a possibility of improving this by leveraging a structural nature of academics abstracts (e.g., first sentence of the abstract is almost always about the general field of study; last few sentences mostly discuss specific details that are unique to the paper) to diversify the selection of presented information.
Next steps would include addressing the feedback to improve the tool, and running a scaled-up controlled study with domain experts to rigorously validate the usefulness of the tool. The gold-standard datasct (Stelmakh) ct al. 2023) contains multiple paper-reviewer assignments that can be utilized to design such a study. However, one needs to modify the task format that it accounts for calibration issues, the scores given in the
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Thaiiing Published in Transactions on Machine Learning Research (07/2023)
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Figure 19: R2P2 then allows users to interactively explore more details for each of the reviewer's paper. One can select a particular paper, select a particular sentence from the submission abstract, to see which sentences of the paper's abstract are the most relevant to the selected sentence.
datasets will be different from scores given by the participants. Additionally, setting up the study would require more care due to the high cost of recruiting domain experts in scale.
29


Reviews:
[{"review_id": "otHgGcZDVz", "reviewer": "Reviewer_A385", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "I think that the paper is concise, clear, straight to the point.\nThe only limitation here is that there is quite limited details about the experimental setup (e.g., setting of the algorithms, etc.) ", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "Paper deals with the combination of expertise with predictions from machine learning models to identify relevant matches in applications such ad peer review or hiring using assistive information about the model outputs to facilitate their decisions.\nPaper, propose a method that allows us to evaluate which kinds of assistive information improve decision makers’ performance (in terms of accuracy and time). \nThrough and experimental study with 271 participants authors\n- show that black-box model explanations reduces users’ accuracy on the matching task, contrary to the commonly-held\nbelief that they can be helpful by allowing better understanding of the model. \n- using custom methods that are designed to closely attend to some task-specific desiderata are found to be effective in improving user performance\n- show that the users’ perceived utility of assistive information is misaligned with their objective utility", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "I think that authors should include in the appendix more details about he setting ofthe algorithms to obtain the results (e.g., hyperparameters search, etc.).", "broader_impact_concerns": "I do not see any ethical concerns.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "BdUJFlFPtd", "reviewer": "Reviewer_pwvc", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\nthe research question faced by the authors is interesting since it is unclear how to integrate humans and machine learning techniques in practical scenarios.\n\nWeaknesses:\nFirst, the authors present the considered document matching task as a proxy task for other decision-making processes such as academic peer review or HR. It is not clear how well this proxy task is aligned to these other tasks, and how well the proposed techniques can be applied to these other domains (e.g. curricula are way different from the documents considered in the paper).\n\nAn important point for my following comments is that the three texts are chosen using a language model: 1) the true text associated to the summary and 2) the two highest scoring, incorrect texts (i.e. texts that would be associated with a different summary).\n\nThe experimental setup considers two settings: easy and hard.\n\nIn the easy setting, the correct text is always associated with the highest score, while in the hard setting this is almost never the case.\n\nAuthors do not spend much time underlining this difference. They even do not consider the easy setting in the considered research hypotheses. However, I think it is of key importance.\nIn the easy setting, the model used to select the sentences is correct. \nThus, methods that explain the model decision work well. In fact, from Figure 6 the performances of the different methods are more or less in line.\n\nIn the hard task, the model is mainly wrong. Thus, the explanation methods considered are \"justifications\" for the (wrong) model scores. It doesn't surprise me that those explanations are confusing for a user. Moreover, the scores are not aligned with the assistive information.\nIn fact, the two ad-hoc methods proposed by the authors and BERTsum can be seen as building blocks of an alternative model.\nFor BERTsum, one can compute a score between the generated summary and the ground-truth one.\nThe proposed models would perform hard (Co-occurrence) or soft (Semantic) matching between the summary and the texts. One can then figure out a way to compute an overall similarity score from those measures. I would expect this model to perform better than the adopted DistilBART.\n\nThe main consideration is that the considered methods do not reflect the score shown to the user, but would reflect another induced score that is not shown. How would users behave when the score is actually aligned with the assistive information?\n\n\nMinor comments:\n\nPage 3: please add more details about the ROUGE metric.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The paper considers the problem of document matching. In the designed task, participants are given a summary and have to choose one between three possible texts that could have generated the summary.", "claims_and_evidence_yn": "No", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "-Analyze more in depth the easy task as well\n-consider enriching the experimental setup, including scores that match the adopted assistive technique", "broader_impact_concerns": "-possible application to hiring procedure.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "aGIWa1Hy6Z", "reviewer": "Reviewer_k9gw", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "See the above comments.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The study finds a surprising result that providing users with black box explanations reduces their accuracy in matching tasks, contrary to conventional intuition. Additionally, the author finds that user's perception of the assistance and its objective utility is misaligned.\n\nHowever, I have some concerns. To ensure the generalizability of the conclusions, it would be worthwhile to consider the performance of SHAP and BERTSum, two general methods, and two task-specific methods on other proxy tasks, which may yield different results. Furthermore, the details of SHAP and BERTSum methods were not discussed, and it is unclear how the model's effectiveness is guaranteed.\n\nThe author mentioned that SHAP's performance varies in different downstream tasks, so other stable SOTA methods should be added for comparison. In addition, the author highlighted relevant information in the candidate article without considering users' acceptance of this method itself, so it may be beneficial to add other forms of assistance.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "No", "audience_explanation": "", "requested_changes": "See the above comments.", "broader_impact_concerns": "The paper may have limited impacts in the machine learning community.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: The paper addresses a relevant and timely issue concerning the application of machine learning techniques to support human activities for a specific class of applications involving document matching. Although the study focuses on a specific task, the findings of the described study are clearly described, supported by a  good amount of experimental evidence, and already by themselves constitute an interesting contribution to the area. Some presentation issues raised by the reviewers have been properly addressed by the revision of the paper.
Decision: Accept (poster)