prompt_front: You are given a scientific article and a question. Answer the question as concisely as you can, using a single phrase or sentence if possible. If the question cannot be answered based on the information in the article, write "unanswerable". If the question is a yes/no question, answer "yes", "no", or "unanswerable". Do not provide any explanation.

Please answer the questions based on the text provided below. What are the clinical datasets used in the paper?
Context 1: Introduction
During the first two decades of the 21st century, the sharing and processing of vast amounts of data has become pervasive  This expansion of data sharing and processing capabilities is both a blessing and a curse  Data helps build better information systems for the digital era and enables further research for advanced data management that benefits the society in general  But the use of this very data containing sensitive information conflicts with private data protection, both from an ethical and a legal perspective 
There are several application domains on which this situation is particularly acute  This is the case of the medical domain BIBREF0  There are plenty of potential applications for advanced medical data management that can only be researched and developed using real data; yet, the use of medical data is severely limited –when not entirely prohibited– due to data privacy protection policies 
One way of circumventing this problem is to anonymise the data by removing, replacing or obfuscating the personal information mentioned, as exemplified in Table TABREF1  This task can be done by hand, having people read and anonymise the documents one by one  Despite being a reliable and simple solution, this approach is tedious, expensive, time consuming and difficult to scale to the potentially thousands or millions of documents that need to be anonymised 
For this reason, numerous of systems and approaches have been developed during the last decades to attempt to automate the anonymisation of sensitive content, starting with the automatic detection and classification of sensitive information  Some of these systems rely on rules, patterns and dictionaries, while others use more advanced techniques related to machine learning and, more recently, deep learning 
Given that this paper is concerned with text documents (e g  medical records), the involved techniques are related to Natural Language Processing (NLP)  When using NLP approaches, it is common to pose the problem of document anonymisation as a sequence labelling problem, i e  classifying each token within a sequence as being sensitive information or not  Further, depending on the objective of the anonymisation task, it is also important to determine the type of sensitive information (names of individuals, addresses, age, sex, etc ) 
The anonymisation systems based on NLP techniques perform reasonably well, but are far from perfect  Depending on the difficulty posed by each dataset or the amount of available data for training machine learning models, the performance achieved by these methods is not enough to fully rely on them in certain situations BIBREF0  However, in the last two years, the NLP community has reached an important milestone thanks to the appearance of the so-called Transformers neural network architectures BIBREF1  In this paper, we conduct several experiments in sensitive information detection and classification on Spanish clinical text using BERT (from `Bidirectional Encoder Representations from Transformers') BIBREF2 as the base for a sequence labelling approach  The experiments are carried out on two datasets: the MEDDOCAN: Medical Document Anonymization shared task dataset BIBREF3, and NUBes BIBREF4, a corpus of real medical reports in Spanish  In these experiments, we compare the performance of BERT with other machine-learning-based systems, some of which use language-specific features  Our aim is to evaluate how good a BERT-based model performs without language nor domain specialisation apart from the training data labelled for the task at hand 
The rest of the paper is structured as follows: the next section describes related work about data anonymisation in general and clinical data anonymisation in particular; it also provides a more detailed explanation and background about the Transformers architecture and BERT  Section SECREF3 describes the data involved in the experiments and the systems evaluated in this paper, including the BERT-based system; finally, it details the experimental design  Section SECREF4 introduces the results for each set of experiments  Finally, Section SECREF5 contains the conclusions and future lines of work 
Related Work
The state of the art in the field of Natural Language Processing (NLP) has reached an important milestone in the last couple of years thanks to deep-learning architectures, increasing in several points the performance of new models for almost any text processing task 
The major change started with the Transformers model proposed by vaswani2017attention  It substituted the widely used recurrent and convolutional neural network architectures by another approach based solely on self-attention, obtaining an impressive performance gain  The original proposal was focused on an encoder-decoder architecture for machine translation, but soon the use of Transformers was made more general BIBREF1  There are several other popular models that use Transformers, such as Open AI's GPT and GPT2 BIBREF5, RoBERTa BIBREF6 and the most recent XLNet BIBREF7; still, BERT BIBREF2 is one of the most widespread Transformer-based models 
BERT trains its unsupervised language model using a Masked Language Model and Next Sentence Prediction  A common problem in NLP is the lack of enough training data  BERT can be pre-trained to learn general or specific language models using very large amounts of unlabelled text (e g  web content, Wikipedia, etc ), and this knowledge can be transferred to a different downstream task in a process that receives the name fine-tuning 
devlin2018bert have used fine-tuning to achieve state-of-the-art results on a wide variety of challenging natural language tasks, such as text classification, Question Answering (QA) and Named Entity Recognition and Classification (NERC)  BERT has also been used successfully by other community practitioners for a wide range of NLP-related tasks BIBREF8, BIBREF9 
Regarding the task of data anonymisation in particular, anonymisation systems may follow different approaches and pursue different objectives (Cormode and Srivastava, 2009)  The first objective of these systems is to detect and classify the sensitive information contained in the documents to be anonymised  In order to achieve that, they use rule-based approaches, Machine Learning (ML) approaches, or a combination of both 
Although most of these efforts are for English texts –see, among others, the i2b2 de-identification challenges BIBREF10, BIBREF11, dernon2016deep, or khin2018deep–, other languages are also attracting growing interest  Some examples are mamede2016automated for Portuguese and tveit2004anonymization for Norwegian  With respect to the anonymisation of text written in Spanish, recent studies include medina2018building, hassan2018anonimizacion and garcia2018automating  Most notably, in 2019 the first community challenge about anonymisation of medical documents in Spanish, MEDDOCAN BIBREF3, was held as part of the IberLEF initiative  The winners of the challenge –the Neither-Language-nor-Domain-Experts (NLNDE) BIBREF12– achieved F1-scores as high as 0 975 in the task of sensitive information detection and categorisation by using recurrent neural networks with Conditional Random Field (CRF) output layers 
At the same challenge, mao2019hadoken occupied the 8th position among 18 participants using BERT  According to the description of the system, the authors used BERT-Base Multilingual Cased and an output CRF layer  However, their system is $\sim $3 F1-score points below our implementation without the CRF layer 
Materials and Methods
The aim of this paper is to evaluate BERT's multilingual model and compare it to other established machine-learning algorithms in a specific task: sensitive data detection and classification in Spanish clinical free text  This section describes the data involved in the experiments and the systems evaluated  Finally, we introduce the experimental setup 
Materials and Methods ::: Data
Two datasets are exploited in this article  Both datasets consist of plain text containing clinical narrative written in Spanish, and their respective manual annotations of sensitive information in BRAT BIBREF13 standoff format  In order to feed the data to the different algorithms presented in Section SECREF7, these datasets were transformed to comply with the commonly used BIO sequence representation scheme BIBREF14 
Materials and Methods ::: Data ::: NUBes-PHI
NUBes BIBREF4 is a corpus of around 7,000 real medical reports written in Spanish and annotated with negation and uncertainty information  Before being published, sensitive information had to be manually annotated and replaced for the corpus to be safely shared  In this article, we work with the NUBes version prior to its anonymisation, that is, with the manual annotations of sensitive information  It follows that the version we work with is not publicly available and, due to contractual restrictions, we cannot reveal the provenance of the data  In order to avoid confusion between the two corpus versions, we henceforth refer to the version relevant in this paper as NUBes-PHI (from `NUBes with Personal Health Information') 
NUBes-PHI consists of 32,055 sentences annotated for 11 different sensitive information categories  Overall, it contains 7,818 annotations  The corpus has been randomly split into train (72%), development (8%) and test (20%) sets to conduct the experiments described in this paper  The size of each split and the distribution of the annotations can be consulted in Tables and , respectively 
The majority of sensitive information in NUBes-PHI are temporal expressions (`Date' and `Time'), followed by healthcare facility mentions (`Hospital'), and the age of the patient  Mentions of people are not that frequent, with physician names (`Doctor') occurring much more often than patient names (`Patient')  The least frequent sensitive information types, which account for $\sim $10% of the remaining annotations, consist of the patient's sex, job, and kinship, and locations other than healthcare facilities (`Location')  Finally, the tag `Other' includes, for instance, mentions to institutions unrelated to healthcare and whether the patient is right- or left-handed  It occurs just 36 times 
Materials and Methods ::: Data ::: The MEDDOCAN corpus
The organisers of the MEDDOCAN shared task BIBREF3 curated a synthetic corpus of clinical cases enriched with sensitive information by health documentalists  In this regard, the MEDDOCAN evaluation scenario could be said to be somewhat far from the real use case the technology developed for the shared task is supposed to be applied in  However, at the moment it also provides the only public means for a rigorous comparison between systems for sensitive health information detection in Spanish texts 
The size of the MEDDOCAN corpus is shown in Table   Compared to NUBes-PHI (Table ), this corpus contains more sensitive information annotations, both in absolute and relative terms 
The sensitive annotation categories considered in MEDDOCAN differ in part from those in NUBes-PHI  Most notably, it contains finer-grained labels for location-related mentions –namely, `Address', `Territory', and `Country'–, and other sensitive information categories that we did not encounter in NUBes-PHI (e g , identifiers, phone numbers, e-mail addresses, etc )  In total, the MEDDOCAN corpus has 21 sensitive information categories  We refer the reader to the organisers' article BIBREF3 for more detailed information about this corpus 
Materials and Methods ::: Systems
Apart from experimenting with a pre-trained BERT model, we have run experiments with other systems and baselines, to compare them and obtain a better perspective about BERT's performance in these datasets 
Materials and Methods ::: Systems ::: Baseline
As the simplest baseline, a sensitive data recogniser and classifier has been developed that consists of regular-expressions and dictionary look-ups  For each category to detect a specific method has been implemented  For instance, the Date, Age, Time and Doctor detectors are based on regular-expressions; Hospital, Sex, Kinship, Location, Patient and Job are looked up in dictionaries  The dictionaries are hand-crafted from the training data available, except for the Patient's case, for which the possible candidates considered are the 100 most common female and male names in Spain according to the Instituto Nacional de Estadística (INE; Spanish Statistical Office) 
Materials and Methods ::: Systems ::: CRF
Conditional Random Fields (CRF) BIBREF15 have been extensively used for tasks of sequential nature  In this paper, we propose as one of the competitive baselines a CRF classifier trained with sklearn-crfsuite for Python 3 5 and the following configuration: algorithm = lbfgs; maximum iterations = 100; c1 = c2 = 0 1; all transitions = true; optimise = false  The features extracted from each token are as follows:
[noitemsep]
prefixes and suffixes of 2 and 3 characters;
the length of the token in characters and the length of the sentence in tokens;
whether the token is all-letters, a number, or a sequence of punctuation marks;
whether the token contains the character `@';
whether the token is the start or end of the sentence;
the token's casing and the ratio of uppercase characters, digits, and punctuation marks to its length;
and, the lemma, part-of-speech tag, and named-entity tag given by ixa-pipes BIBREF16 upon analysing the sentence the token belongs to 
Noticeably, none of the features used to train the CRF classifier is domain-dependent  However, the latter group of features is language dependent 
Materials and Methods ::: Systems ::: spaCy
spaCy is a widely used NLP library that implements state-of-the-art text processing pipelines, including a sequence-labelling pipeline similar to the one described by strubell2017fast  spaCy offers several pre-trained models in Spanish, which perform basic NLP tasks such as Named Entity Recognition (NER)  In this paper, we have trained a new NER model to detect NUBes-PHI labels  For this purpose, the new model uses all the labels of the training corpus coded with its context at sentence level  The network optimisation parameters and dropout values are the ones recommended in the documentation for small datasets  Finally, the model is trained using batches of size 64  No more features are included, so the classifier is language-dependent but not domain-dependent 
Materials and Methods ::: Systems ::: BERT
As introduced earlier, BERT has shown an outstanding performance in NERC-like tasks, improving the start-of-the-art results for almost every dataset and language  We take the same approach here, by using the model BERT-Base Multilingual Cased with a Fully Connected (FC) layer on top to perform a fine-tuning of the whole model for an anonymisation task in Spanish clinical data  Our implementation is built on PyTorch and the PyTorch-Transformers library BIBREF1  The training phase consists in the following steps (roughly depicted in Figure ):
Pre-processing: since we are relying on a pre-trained BERT model, we must match the same configuration by using a specific tokenisation and vocabulary  BERT also needs that the inputs contains special tokens to signal the beginning and the end of each sequence 
Fine-tuning: the pre-processed sequence is fed into the model  BERT outputs the contextual embeddings that encode each of the inputted tokens  This embedding representation for each token is fed into the FC linear layer after a dropout layer (with a 0 1 dropout probability), which in turn outputs the logits for each possible class  The cross-entropy loss function is calculated comparing the logits and the gold labels, and the error is back-propagated to adjust the model parameters 
We have trained the model using an AdamW optimiser BIBREF17 with the learning rate set to 3e-5, as recommended by devlin2018bert, and with a gradient clipping of 1 0  We also applied a learning-rate scheduler that warms up the learning rate from zero to its maximum value as the training progresses, which is also a common practice  For each experiment set proposed below, the training was run with an early-stopping patience of 15 epochs  Then, the model that performed best against the development set was used to produce the reported results 
The experiments were run on a 64-core server with operating system Ubuntu 16 04, 250GB of RAM memory, and 4 GeForce RTX 2080 GPUs with 11GB of memory  The maximum sequence length was set at 500 and the batch size at 12  In this setting, each epoch –a full pass through all the training data– required about 10 minutes to complete 
Materials and Methods ::: Experimental design
We have conducted experiments with BERT in the two datasets of Spanish clinical narrative presented in Section SECREF3 The first experiment set uses NUBes-PHI, a corpus of real medical reports manually annotated with sensitive information  Because this corpus is not publicly available, and in order to compare the BERT-based model to other related published systems, the second set of experiments uses the MEDDOCAN 2019 shared task competition dataset  The following sections provide greater detail about the two experimental setups 
Materials and Methods ::: Experimental design ::: Experiment A: NUBes-PHI
In this experiment set, we evaluate all the systems presented in Section SECREF7, namely, the rule-based baseline, the CRF classifier, the spaCy entity tagger, and BERT  The evaluation comprises three scenarios of increasing difficulty:
[noitemsep]
- Evaluates the performance of the systems at predicting whether each token is sensitive or non-sensitive; that is, the measurements only take into account whether a sensitive token has been recognised or not, regardless of the BIO label and the category assigned  This scenario shows how good a system would be at obfuscating sensitive data (e g , by replacing sensitive tokens with asterisks) 
- We measure the performance of the systems at predicting the sensitive information type of each token –i e , the 11 categories presented in Section SECREF5 or `out'  Detecting entity types correctly is important if a system is going to be used to replace sensitive data by fake data of the same type (e g , random people names) 
- This is the strictest evaluation, as it takes into account both the BIO label and the category assigned to each individual token  Being able to discern between two contiguous sensitive entities of the same type is relevant not only because it is helpful when producing fake replacements, but because it also yields more accurate statistics of the sensitive information present in a given document collection 
The systems are evaluated in terms of micro-average precision, recall and F1-score in all the scenarios 
In addition to the scenarios proposed, a subject worth being studied is the need of labelled data  Manually labelled data is an scarce and expensive resource, which for some application domains or languages is difficult to come by  In order to obtain an estimation of the dependency of each system on the available amount of training data, we have retrained all the compared models using decreasing amounts of data –from 100% of the available training instances to just 1%  The same data subsets have been used to train all the systems  Due to the knowledge transferred from the pre-trained BERT model, the BERT-based model is expected to be more robust to data scarcity than those that start their training from scratch 
Materials and Methods ::: Experimental design ::: Experiment B: MEDDOCAN
In this experiment set, our BERT implementation is compared to several systems that participated in the MEDDOCAN challenge: a CRF classifier BIBREF18, a spaCy entity recogniser BIBREF18, and NLNDE BIBREF12, the winner of the shared task and current state of the art for sensitive information detection and classification in Spanish clinical text  Specifically, we include the results of a domain-independent NLNDE model (S2), and the results of a model enriched with domain-specific embeddings (S3)  Finally, we include the results obtained by mao2019hadoken with a CRF output layer on top of BERT embeddings  MEDDOCAN consists of two scenarios:
[noitemsep]
- This evaluation measures how good a system is at detecting sensitive text spans, regardless of the category assigned to them 
- In this scenario, systems are required to match exactly not only the boundaries of each sensitive span, but also the category assigned 
The systems are evaluated in terms of micro-averaged precision, recall and F-1 score  Note that, in contrast to the evaluation in Experiment A, MEDDOCAN measurements are entity-based instead of tokenwise  An exhaustive explanation of the MEDDOCAN evaluation procedure is available online, as well as the official evaluation script, which we used to obtain the reported results 
Results
This section describes the results obtained in the two sets of experiments: NUBes-PHI and MEDDOCAN 
Results ::: Experiment A: NUBes-PHI
Table shows the results of the conducted experiments in NUBes-PHI for all the compared systems  The included baseline serves to give a quick insight about how challenging the data is  With simple regular expressions and gazetteers a precision of 0 853 is obtained  On the other hand, the recall, which directly depends on the coverage provided by the rules and resources, drops to 0 469  Hence, this task is unlikely to be solved without the generalisation capabilities provided by machine-learning and deep-learning models 
Regarding the detection scenario –that is, the scenario concerned with a binary classification to determine whether each individual token conveys sensitive information or not–, it can be observed that BERT outperforms its competitors  A fact worth highlighting is that, according to these results, BERT achieves a precision lower than the rest of the systems (i e , it makes more false positive predictions); in exchange, it obtains a remarkably higher recall  Noticeably, it reaches a recall of 0 979, improving by more than 4 points the second-best system, spaCy 
The table also shows the results for the relaxed metric that only takes into account the entity type detected, regardless of the BIO label (i e , ignoring whether the token is at the beginning or in the middle of a sensitive sequence of tokens)  The conclusions are very similar to those extracted previously, with BERT gaining 2 1 points of F1-score over the CRF based approach  The confusion matrices of the predictions made by CRF, spaCy, and BERT in this scenario are shown in Table   As can bee seen, BERT has less difficulty in predicting correctly less frequent categories, such as `Location', `Job', and `Patient'  One of the most common mistakes according to the confusion matrices is classifying hospital names as `Location' instead of the more accurate `Hospital'; this is hardly a harmful error, given that a hospital is actually a location  Last, the category `Other' is completely leaked by all the compared systems, most likely due to its almost total lack of support in both training and evaluation datasets 
To finish with this experiment set, Table also shows the strict classification precision, recall and F1-score for the compared systems  Despite the fact that, in general, the systems obtain high values, BERT outperforms them again  BERT's F1-score is 1 9 points higher than the next most competitive result in the comparison  More remarkably, the recall obtained by BERT is about 5 points above 
Upon manual inspection of the errors committed by the BERT-based model, we discovered that it has a slight tendency towards producing ill-formed BIO sequences (e g, starting a sensitive span with `Inside' instead of `Begin'; see Table )  We could expect that complementing the BERT-based model with a CRF layer on top would help enforce the emission of valid sequences, alleviating this kind of errors and further improving its results 
Finally, Figure shows the impact of decreasing the amount of training data in the detection scenario  It shows the difference in precision, recall, and F1-score with respect to that obtained using 100% of the training data  A general downward trend can be observed, as one would expect: less training data leads to less accurate predictions  However, the BERT-based model is the most robust to training-data reduction, showing an steadily low performance loss  With 1% of the dataset (230 training instances), the BERT-based model only suffers a striking 7-point F1-score loss, in contrast to the 32 and 39 points lost by the CRF and spaCy models, respectively  This steep performance drop stems to a larger extent from recall decline, which is not that marked in the case of BERT  Overall, these results indicate that the transfer-learning achieved through the BERT multilingual pre-trained model not only helps obtain better results, but also lowers the need of manually labelled data for this application domain 
Results ::: Experiment B: MEDDOCAN
The results of the two MEDDOCAN scenarios –detection and classification– are shown in Table   These results follow the same pattern as in the previous experiments, with the CRF classifier being the most precise of all, and BERT outperforming both the CRF and spaCy classifiers thanks to its greater recall  We also show the results of mao2019hadoken who, despite of having used a BERT-based system, achieve lower scores than our models  The reason why it should be so remain unclear 
With regard to the winner of the MEDDOCAN shared task, the BERT-based model has not improved the scores obtained by neither the domain-dependent (S3) nor the domain-independent (S2) NLNDE model  However, attending to the obtained results, BERT remains only 0 3 F1-score points behind, and would have achieved the second position among all the MEDDOCAN shared task competitors  Taking into account that only 3% of the gold labels remain incorrectly annotated, the task can be considered almost solved, and it is not clear if the differences among the systems are actually significant, or whether they stem from minor variations in initialisation or a long-tail of minor labelling inconsistencies 
Conclusions and Future Work
In this work we have briefly introduced the problems related to data privacy protection in clinical domain  We have also described some of the groundbreaking advances on the Natural Language Processing field due to the appearance of Transformers-based deep-learning architectures and transfer learning from very large general-domain multilingual corpora, focusing our attention in one of its most representative examples, Google's BERT model 
In order to assess the performance of BERT for Spanish clinical data anonymisation, we have conducted several experiments with a BERT-based sequence labelling approach using the pre-trained multilingual BERT model shared by Google as the starting point for the model training  We have compared this BERT-based sequence labelling against other methods and systems  One of the experiments uses the MEDDOCAN 2019 shared task dataset, while the other uses a novel Spanish clinical reports dataset called NUBes-PHI 
The results of the experiments show that, in NUBes-PHI, the BERT-based model outperforms the other systems without requiring any adaptation or domain-specific feature engineering, just by being trained on the provided labelled data  Interestingly, the BERT-based model obtains a remarkably higher recall than the other systems  High recall is a desirable outcome because, when anonymising sensible documents, the accidental leak of sensible data is likely to be more dangerous than the unintended over-obfuscation of non-sensitive text 
Further, we have conducted an additional experiment on this dataset by progressively reducing the training data for all the compared systems  The BERT-based model shows the highest robustness to training-data scarcity, loosing only 7 points of F1-score when trained on 230 instances instead of 21,371  These observation are in line with the results obtained by the NLP community using BERT for other tasks 
The experiments with the MEDDOCAN 2019 shared task dataset follow the same pattern  In this case, the BERT-based model falls 0 3 F1-score points behind the shared task winning system, but it would have achieved the second position in the competition with no further refinement 
Since we have used a pre-trained multilingual BERT model, the same approach is likely to work for other languages just by providing some labelled training data  Further, this is the simplest fine-tuning that can be performed based on BERT  More sophisticated fine-tuning layers could help improve the results  For example, it could be expected that a CRF layer helped enforce better BIO tagging sequence predictions  Precisely, mao2019hadoken participated in the MEDDOCAN competition using a BERT+CRF architecture, but their reported scores are about 3 points lower than our implementation  From the description of their work, it is unclear what the source of this score difference could be 
Further, at the time of writing this paper, new multilingual pre-trained models and Transformer architectures have become available  It would not come as a surprise that these new resources and systems –e g , XLM-RoBERTa BIBREF19 or BETO BIBREF20, a BERT model fully pre-trained on Spanish texts– further advanced the state of the art in this task 
Acknowledgements
This work has been supported by Vicomtech and partially funded by the project DeepReading (RTI2018-096846-B-C21, MCIU/AEI/FEDER,UE) 

ground_truth: MEDDOCAN, NUBes-PHI

MEDDOCAN, NUBes 

prompt_front: You are given a scientific article and a question. Answer the question as concisely as you can, using a single phrase or sentence if possible. If the question cannot be answered based on the information in the article, write "unanswerable". If the question is a yes/no question, answer "yes", "no", or "unanswerable". Do not provide any explanation.

Please answer the questions based on the text provided below. What summarization algorithms did the authors experiment with?
Context 2: Introduction
Performance appraisal (PA) is an important HR process, particularly for modern organizations that crucially depend on the skills and expertise of their workforce  The PA process enables an organization to periodically measure and evaluate every employee's performance  It also provides a mechanism to link the goals established by the organization to its each employee's day-to-day activities and performance  Design and analysis of PA processes is a lively area of research within the HR community BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3  
The PA process in any modern organization is nowadays implemented and tracked through an IT system (the PA system) that records the interactions that happen in various steps  Availability of this data in a computer-readable database opens up opportunities to analyze it using automated statistical, data-mining and text-mining techniques, to generate novel and actionable insights / patterns and to help in improving the quality and effectiveness of the PA process BIBREF4 , BIBREF5 , BIBREF6   Automated analysis of large-scale PA data is now facilitated by technological and algorithmic advances, and is becoming essential for large organizations containing thousands of geographically distributed employees handling a wide variety of roles and tasks 
A typical PA process involves purposeful multi-step multi-modal communication between employees, their supervisors and their peers  In most PA processes, the communication includes the following steps: (i) in self-appraisal, an employee records his/her achievements, activities, tasks handled etc ; (ii) in supervisor assessment, the supervisor provides the criticism, evaluation and suggestions for improvement of performance etc ; and (iii) in peer feedback (aka INLINEFORM0 view), the peers of the employee provide their feedback  There are several business questions that managers are interested in  Examples:
In this paper, we develop text mining techniques that can automatically produce answers to these questions  Since the intended users are HR executives, ideally, the techniques should work with minimum training data and experimentation with parameter setting  These techniques have been implemented and are being used in a PA system in a large multi-national IT company 
The rest of the paper is organized as follows  Section SECREF2 summarizes related work  Section SECREF3 summarizes the PA dataset used in this paper  Section SECREF4 applies sentence classification algorithms to automatically discover three important classes of sentences in the PA corpus viz , sentences that discuss strengths, weaknesses of employees and contain suggestions for improving her performance  Section SECREF5 considers the problem of mapping the actual targets mentioned in strengths, weaknesses and suggestions to a fixed set of attributes  In Section SECREF6 , we discuss how the feedback from peers for a particular employee can be summarized  In Section SECREF7 we draw conclusions and identify some further work 
Related Work
We first review some work related to sentence classification  Semantically classifying sentences (based on the sentence's purpose) is a much harder task, and is gaining increasing attention from linguists and NLP researchers  McKnight and Srinivasan BIBREF7 and Yamamoto and Takagi BIBREF8 used SVM to classify sentences in biomedical abstracts into classes such as INTRODUCTION, BACKGROUND, PURPOSE, METHOD, RESULT, CONCLUSION  Cohen et al  BIBREF9 applied SVM and other techniques to learn classifiers for sentences in emails into classes, which are speech acts defined by a verb-noun pair, with verbs such as request, propose, amend, commit, deliver and nouns such as meeting, document, committee; see also BIBREF10   Khoo et al  BIBREF11 uses various classifiers to classify sentences in emails into classes such as APOLOGY, INSTRUCTION, QUESTION, REQUEST, SALUTATION, STATEMENT, SUGGESTION, THANKING etc  Qadir and Riloff BIBREF12 proposes several filters and classifiers to classify sentences on message boards (community QA systems) into 4 speech acts: COMMISSIVE (speaker commits to a future action), DIRECTIVE (speaker expects listener to take some action), EXPRESSIVE (speaker expresses his or her psychological state to the listener), REPRESENTATIVE (represents the speaker's belief of something)  Hachey and Grover BIBREF13 used SVM and maximum entropy classifiers to classify sentences in legal documents into classes such as FACT, PROCEEDINGS, BACKGROUND, FRAMING, DISPOSAL; see also BIBREF14   Deshpande et al  BIBREF15 proposes unsupervised linguistic patterns to classify sentences into classes SUGGESTION, COMPLAINT 
There is much work on a closely related problem viz , classifying sentences in dialogues through dialogue-specific categories called dialogue acts BIBREF16 , which we will not review here  Just as one example, Cotterill BIBREF17 classifies questions in emails into the dialogue acts of YES_NO_QUESTION, WH_QUESTION, ACTION_REQUEST, RHETORICAL, MULTIPLE_CHOICE etc 
We could not find much work related to mining of performance appraisals data  Pawar et al  BIBREF18 uses kernel-based classification to classify sentences in both performance appraisal text and product reviews into classes SUGGESTION, APPRECIATION, COMPLAINT  Apte et al  BIBREF6 provides two algorithms for matching the descriptions of goals or tasks assigned to employees to a standard template of model goals  One algorithm is based on the co-training framework and uses goal descriptions and self-appraisal comments as two separate perspectives  The second approach uses semantic similarity under a weak supervision framework  Ramrakhiyani et al  BIBREF5 proposes label propagation algorithms to discover aspects in supervisor assessments in performance appraisals, where an aspect is modelled as a verb-noun pair (e g  conduct training, improve coding) 
Dataset
In this paper, we used the supervisor assessment and peer feedback text produced during the performance appraisal of 4528 employees in a large multi-national IT company  The corpus of supervisor assessment has 26972 sentences  The summary statistics about the number of words in a sentence is: min:4 max:217 average:15 5 STDEV:9 2 Q1:9 Q2:14 Q3:19 
Sentence Classification
The PA corpus contains several classes of sentences that are of interest  In this paper, we focus on three important classes of sentences viz , sentences that discuss strengths (class STRENGTH), weaknesses of employees (class WEAKNESS) and suggestions for improving her performance (class SUGGESTION)  The strengths or weaknesses are mostly about the performance in work carried out, but sometimes they can be about the working style or other personal qualities  The classes WEAKNESS and SUGGESTION are somewhat overlapping; e g , a suggestion may address a perceived weakness  Following are two example sentences in each class 
STRENGTH:
WEAKNESS:
SUGGESTION:
Several linguistic aspects of these classes of sentences are apparent  The subject is implicit in many sentences  The strengths are often mentioned as either noun phrases (NP) with positive adjectives (Excellent technology leadership) or positive nouns (engineering strength) or through verbs with positive polarity (dedicated) or as verb phrases containing positive adjectives (delivers innovative solutions)  Similarly for weaknesses, where negation is more frequently used (presentations are not his forte), or alternatively, the polarities of verbs (avoid) or adjectives (poor) tend to be negative  However, sometimes the form of both the strengths and weaknesses is the same, typically a stand-alone sentiment-neutral NP, making it difficult to distinguish between them; e g , adherence to timing or timely closure  Suggestions often have an imperative mood and contain secondary verbs such as need to, should, has to  Suggestions are sometimes expressed using comparatives (better process compliance)  We built a simple set of patterns for each of the 3 classes on the POS-tagged form of the sentences  We use each set of these patterns as an unsupervised sentence classifier for that class  If a particular sentence matched with patterns for multiple classes, then we have simple tie-breaking rules for picking the final class  The pattern for the STRENGTH class looks for the presence of positive words / phrases like takes ownership, excellent, hard working, commitment, etc  Similarly, the pattern for the WEAKNESS class looks for the presence of negative words / phrases like lacking, diffident, slow learner, less focused, etc  The SUGGESTION pattern not only looks for keywords like should, needs to but also for POS based pattern like “a verb in the base form (VB) in the beginning of a sentence” 
We randomly selected 2000 sentences from the supervisor assessment corpus and manually tagged them (dataset D1)  This labelled dataset contained 705, 103, 822 and 370 sentences having the class labels STRENGTH, WEAKNESS, SUGGESTION or OTHER respectively  We trained several multi-class classifiers on this dataset  Table TABREF10 shows the results of 5-fold cross-validation experiments on dataset D1  For the first 5 classifiers, we used their implementation from the SciKit Learn library in Python (scikit-learn org)  The features used for these classifiers were simply the sentence words along with their frequencies  For the last 2 classifiers (in Table TABREF10 ), we used our own implementation  The overall accuracy for a classifier is defined as INLINEFORM0 , where the denominator is 2000 for dataset D1  Note that the pattern-based approach is unsupervised i e , it did not use any training data  Hence, the results shown for it are for the entire dataset and not based on cross-validation 
Comparison with Sentiment Analyzer
We also explored whether a sentiment analyzer can be used as a baseline for identifying the class labels STRENGTH and WEAKNESS  We used an implementation of sentiment analyzer from TextBlob to get a polarity score for each sentence  Table TABREF13 shows the distribution of positive, negative and neutral sentiments across the 3 class labels STRENGTH, WEAKNESS and SUGGESTION  It can be observed that distribution of positive and negative sentiments is almost similar in STRENGTH as well as SUGGESTION sentences, hence we can conclude that the information about sentiments is not much useful for our classification problem 
Discovering Clusters within Sentence Classes
After identifying sentences in each class, we can now answer question (1) in Section SECREF1   From 12742 sentences predicted to have label STRENGTH, we extract nouns that indicate the actual strength, and cluster them using a simple clustering algorithm which uses the cosine similarity between word embeddings of these nouns  We repeat this for the 9160 sentences with predicted label WEAKNESS or SUGGESTION as a single class  Tables TABREF15 and TABREF16 show a few representative clusters in strengths and in weaknesses, respectively  We also explored clustering 12742 STRENGTH sentences directly using CLUTO BIBREF19 and Carrot2 Lingo BIBREF20 clustering algorithms  Carrot2 Lingo discovered 167 clusters and also assigned labels to these clusters  We then generated 167 clusters using CLUTO as well  CLUTO does not generate cluster labels automatically, hence we used 5 most frequent words within the cluster as its labels  Table TABREF19 shows the largest 5 clusters by both the algorithms  It was observed that the clusters created by CLUTO were more meaningful and informative as compared to those by Carrot2 Lingo  Also, it was observed that there is some correspondence between noun clusters and sentence clusters  E g  the nouns cluster motivation expertise knowledge talent skill (Table TABREF15 ) corresponds to the CLUTO sentence cluster skill customer management knowledge team (Table TABREF19 )  But overall, users found the nouns clusters to be more meaningful than the sentence clusters 
PA along Attributes
In many organizations, PA is done from a predefined set of perspectives, which we call attributes  Each attribute covers one specific aspect of the work done by the employees  This has the advantage that we can easily compare the performance of any two employees (or groups of employees) along any given attribute  We can correlate various performance attributes and find dependencies among them  We can also cluster employees in the workforce using their supervisor ratings for each attribute to discover interesting insights into the workforce  The HR managers in the organization considered in this paper have defined 15 attributes (Table TABREF20 )  Each attribute is essentially a work item or work category described at an abstract level  For example, FUNCTIONAL_EXCELLENCE covers any tasks, goals or activities related to the software engineering life-cycle (e g , requirements analysis, design, coding, testing etc ) as well as technologies such as databases, web services and GUI 
In the example in Section SECREF4 , the first sentence (which has class STRENGTH) can be mapped to two attributes: FUNCTIONAL_EXCELLENCE and BUILDING_EFFECTIVE_TEAMS  Similarly, the third sentence (which has class WEAKNESS) can be mapped to the attribute INTERPERSONAL_EFFECTIVENESS and so forth  Thus, in order to answer the second question in Section SECREF1 , we need to map each sentence in each of the 3 classes to zero, one, two or more attributes, which is a multi-class multi-label classification problem 
We manually tagged the same 2000 sentences in Dataset D1 with attributes, where each sentence may get 0, 1, 2, etc  up to 15 class labels (this is dataset D2)  This labelled dataset contained 749, 206, 289, 207, 91, 223, 191, 144, 103, 80, 82, 42, 29, 15, 24 sentences having the class labels listed in Table TABREF20 in the same order  The number of sentences having 0, 1, 2, or more than 2 attributes are: 321, 1070, 470 and 139 respectively  We trained several multi-class multi-label classifiers on this dataset  Table TABREF21 shows the results of 5-fold cross-validation experiments on dataset D2 
Precision, Recall and F-measure for this multi-label classification are computed using a strategy similar to the one described in BIBREF21   Let INLINEFORM0 be the set of predicted labels and INLINEFORM1 be the set of actual labels for the INLINEFORM2 instance  Precision and recall for this instance are computed as follows: INLINEFORM3
It can be observed that INLINEFORM0 would be undefined if INLINEFORM1 is empty and similarly INLINEFORM2 would be undefined when INLINEFORM3 is empty  Hence, overall precision and recall are computed by averaging over all the instances except where they are undefined  Instance-level F-measure can not be computed for instances where either precision or recall are undefined  Therefore, overall F-measure is computed using the overall precision and recall 
Summarization of Peer Feedback using ILP
The PA system includes a set of peer feedback comments for each employee  To answer the third question in Section SECREF1 , we need to create a summary of all the peer feedback comments about a given employee  As an example, following are the feedback comments from 5 peers of an employee 
The individual sentences in the comments written by each peer are first identified and then POS tags are assigned to each sentence  We hypothesize that a good summary of these multiple comments can be constructed by identifying a set of important text fragments or phrases  Initially, a set of candidate phrases is extracted from these comments and a subset of these candidate phrases is chosen as the final summary, using Integer Linear Programming (ILP)  The details of the ILP formulation are shown in Table TABREF36   As an example, following is the summary generated for the above 5 peer comments 
humble nature, effective communication, technical expertise, always supportive, vast knowledge
Following rules are used to identify candidate phrases:
Various parameters are used to evaluate a candidate phrase for its importance  A candidate phrase is more important:
A complete list of parameters is described in detail in Table TABREF36  
There is a trivial constraint INLINEFORM0 which makes sure that only INLINEFORM1 out of INLINEFORM2 candidate phrases are chosen  A suitable value of INLINEFORM3 is used for each employee depending on number of candidate phrases identified across all peers (see Algorithm SECREF6 )  Another set of constraints ( INLINEFORM4 to INLINEFORM5 ) make sure that at least one phrase is selected for each of the leadership attributes  The constraint INLINEFORM6 makes sure that multiple phrases sharing the same headword are not chosen at a time  Also, single word candidate phrases are chosen only if they are adjectives or nouns with lexical category noun attribute  This is imposed by the constraint INLINEFORM7   It is important to note that all the constraints except INLINEFORM8 are soft constraints, i e  there may be feasible solutions which do not satisfy some of these constraints  But each constraint which is not satisfied, results in a penalty through the use of slack variables  These constraints are described in detail in Table TABREF36  
The objective function maximizes the total importance score of the selected candidate phrases  At the same time, it also minimizes the sum of all slack variables so that the minimum number of constraints are broken 
INLINEFORM0 : No  of candidate phrases INLINEFORM1 : No  of phrases to select as part of summary
INLINEFORM0 INLINEFORM1 INLINEFORM2 INLINEFORM3 INLINEFORM4 INLINEFORM5 INLINEFORM6 INLINEFORM7 INLINEFORM8
INLINEFORM0 and INLINEFORM1 INLINEFORM2 INLINEFORM3 INLINEFORM4 INLINEFORM5 INLINEFORM6
INLINEFORM0 (For determining number of phrases to select to include in summary)
Evaluation of auto-generated summaries
We considered a dataset of 100 employees, where for each employee multiple peer comments were recorded  Also, for each employee, a manual summary was generated by an HR personnel  The summaries generated by our ILP-based approach were compared with the corresponding manual summaries using the ROUGE BIBREF22 unigram score  For comparing performance of our ILP-based summarization algorithm, we explored a few summarization algorithms provided by the Sumy package  A common parameter which is required by all these algorithms is number of sentences keep in the final summary  ILP-based summarization requires a similar parameter K, which is automatically decided based on number of total candidate phrases  Assuming a sentence is equivalent to roughly 3 phrases, for Sumy algorithms, we set number of sentences parameter to the ceiling of K/3  Table TABREF51 shows average and standard deviation of ROUGE unigram f1 scores for each algorithm, over the 100 summaries  The performance of ILP-based summarization is comparable with the other algorithms, as the two sample t-test does not show statistically significant difference  Also, human evaluators preferred phrase-based summary generated by our approach to the other sentence-based summaries 
Conclusions and Further Work
In this paper, we presented an analysis of the text generated in Performance Appraisal (PA) process in a large multi-national IT company  We performed sentence classification to identify strengths, weaknesses and suggestions for improvements found in the supervisor assessments and then used clustering to discover broad categories among them  As this is non-topical classification, we found that SVM with ADWS kernel BIBREF18 produced the best results  We also used multi-class multi-label classification techniques to match supervisor assessments to predefined broad perspectives on performance  Logistic Regression classifier was observed to produce the best results for this topical classification  Finally, we proposed an ILP-based summarization technique to produce a summary of peer feedback comments for a given employee and compared it with manual summaries 
The PA process also generates much structured data, such as supervisor ratings  It is an interesting problem to compare and combine the insights from discovered from structured data and unstructured text  Also, we are planning to automatically discover any additional performance attributes to the list of 15 attributes currently used by HR 
Context 3: Introduction
The concept of message passing over graphs has been around for many years BIBREF0, BIBREF1, as well as that of graph neural networks (GNNs) BIBREF2, BIBREF3  However, GNNs have only recently started to be closely investigated, following the advent of deep learning  Some notable examples include BIBREF4, BIBREF5, BIBREF6, BIBREF7, BIBREF8, BIBREF9, BIBREF10, BIBREF11, BIBREF12  These approaches are known as spectral  Their similarity with message passing (MP) was observed by BIBREF9 and formalized by BIBREF13 and BIBREF14 
The MP framework is based on the core idea of recursive neighborhood aggregation  That is, at every iteration, the representation of each vertex is updated based on messages received from its neighbors  All spectral GNNs can be described in terms of the MP framework 
GNNs have been applied with great success to bioinformatics and social network data, for node classification, link prediction, and graph classification  However, a few studies only have focused on the application of the MP framework to representation learning on text  This paper proposes one such application  More precisely, we represent documents as word co-occurrence networks, and develop an expressive MP GNN tailored to document understanding, the Message Passing Attention network for Document understanding (MPAD)  We also propose several hierarchical variants of MPAD  Evaluation on 10 document classification datasets show that our architectures learn representations that are competitive with the state-of-the-art  Furthermore, ablation experiments shed light on the impact of various architectural choices 
In what follows, we first provide some background about the MP framework (in sec  SECREF2), thoroughly describe and explain MPAD (sec  SECREF3), present our experimental framework (sec  SECREF4), report and interpret our results (sec  SECREF5), and provide a review of the relevant literature (sec  SECREF6) 
Message Passing Neural Networks
BIBREF13 proposed a MP framework under which many of the recently introduced GNNs can be reformulated  MP consists in an aggregation phase followed by a combination phase BIBREF14  More precisely, let $G(V,E)$ be a graph, and let us consider $v \in V$  At time $t+1$, a message vector $\mathbf {m}_v^{t+1}$ is computed from the representations of the neighbors $\mathcal {N}(v)$ of $v$:
The new representation $\mathbf {h}^{t+1}_v$ of $v$ is then computed by combining its current feature vector $\mathbf {h}^{t}_v$ with the message vector $\mathbf {m}_v^{t+1}$:
Messages are passed for $T$ time steps  Each step is implemented by a different layer of the MP network  Hence, iterations correspond to network depth  The final feature vector $\mathbf {h}_v^T$ of $v$ is based on messages propagated from all the nodes in the subtree of height $T$ rooted at $v$  It captures both the topology of the neighborhood of $v$ and the distribution of the vertex representations in it 
If a graph-level feature vector is needed, e g , for classification or regression, a READOUT pooling function, that must be invariant to permutations, is applied:
Next, we present the MP network we developed for document understanding 
Message Passing Attention network for Document understanding (MPAD) ::: Word co-occurrence networks
We represent a document as a statistical word co-occurrence network BIBREF18, BIBREF19 with a sliding window of size 2 overspanning sentences  Let us denote that graph $G(V,E)$  Each unique word in the preprocessed document is represented by a node in $G$, and an edge is added between two nodes if they are found together in at least one instantiation of the window  $G$ is directed and weighted: edge directions and weights respectively capture text flow and co-occurrence counts 
$G$ is a compact representation of its document  In $G$, immediate neighbors are consecutive words in the same sentence  That is, paths of length 2 correspond to bigrams  Paths of length more than 2 can correspond either to traditional $n$-grams or to relaxed $n$-grams, that is, words that never appear in the same sentence but co-occur with the same word(s)  Such nodes are linked through common neighbors 
Master node  Inspired by BIBREF3, our $G$ also includes a special document node, linked to all other nodes via unit weight bi-directional edges  In what follows, let us denote by $n$ the number of nodes in $G$, including the master node 
Message Passing Attention network for Document understanding (MPAD) ::: Message passing
We formulate our AGGREGATE function as:
where $\mathbf {H}^t \in \mathbb {R}^{n \times d}$ contains node features ($d$ is a hyperparameter), and $\mathbf {A} \in \mathbb {R}^{n \times n}$ is the adjacency matrix of $G$  Since $G$ is directed, $\mathbf {A}$ is asymmetric  Also, $\mathbf {A}$ has zero diagonal as we choose not to consider the feature of the node itself, only that of its incoming neighbors, when updating its representation  Since $G$ is weighted, the $i^{th}$ row of $A$ contains the weights of the edges incoming on node $v_i$  $\mathbf {D} \in \mathbb {R}^{n \times n}$ is the diagonal in-degree matrix of $G$  MLP denotes a multi-layer perceptron, and $\mathbf {M}^{t+1} \in \mathbb {R}^{n \times d}$ is the message matrix 
The use of a MLP was motivated by the observation that for graph classification, MP neural nets with 1-layer perceptrons are inferior to their MLP counterparts BIBREF14  Indeed, 1-layer perceptrons are not universal approximators of multiset functions  Note that like in BIBREF14, we use a different MLP at each layer 
Renormalization  The rows of $\mathbf {D}^{-1}\mathbf {A}$ sum to 1  This is equivalent to the renormalization trick of BIBREF9, but using only the in-degrees  That is, instead of computing a weighted sum of the incoming neighbors' feature vectors, we compute a weighted average of them  The coefficients are proportional to the strength of co-occurrence between words  One should note that by averaging, we lose the ability to distinguish between different neighborhood structures in some special cases, that is, we lose injectivity  Such cases include neighborhoods in which all nodes have the same representations, and neighborhoods of different sizes containing various representations in equal proportions BIBREF14  As suggested by the results of an ablation experiment, averaging is better than summing in our application (see subsection SECREF30)  Note that instead of simply summing/averaging, we also tried using GAT-like attention BIBREF11 in early experiments, without obtaining better results 
As far as our COMBINE function, we use the Gated Recurrent Unit BIBREF20, BIBREF21:
Omitting biases for readability, we have:
where the $\mathbf {W}$ and $\mathbf {U}$ matrices are trainable weight matrices not shared across time steps, $\sigma (\mathbf {x}) = 1/(1+\exp (-\mathbf {x}))$ is the sigmoid function, and $\mathbf {R}$ and $\mathbf {Z}$ are the parameters of the reset and update gates  The reset gate controls the amount of information from the previous time step (in $\mathbf {H}^t$) that should propagate to the candidate representations, $\tilde{\mathbf {H}}^{t+1}$  The new representations $\mathbf {H}^{t+1}$ are finally obtained by linearly interpolating between the previous and the candidate ones, using the coefficients returned by the update gate 
Interpretation  Updating node representations through a GRU should in principle allow nodes to encode a combination of local and global signals (low and high values of $t$, resp ), by allowing them to remember about past iterations  In addition, we also explicitly consider node representations at all iterations when reading out (see Eq  DISPLAY_FORM18) 
Message Passing Attention network for Document understanding (MPAD) ::: Readout
After passing messages and performing updates for $T$ iterations, we obtain a matrix $\mathbf {H}^T \in \mathbb {R}^{n \times d}$ containing the final vertex representations  Let $\hat{G}$ be graph $G$ without the special document node, and matrix $\mathbf {\hat{H}}^T \in \mathbb {R}^{(n-1) \times d}$ be the corresponding representation matrix (i e , $\mathbf {H}^T$ without the row of the document node) 
We use as our READOUT function the concatenation of self-attention applied to $\mathbf {\hat{H}}^T$ with the final document node representation  More precisely, we apply a global self-attention mechanism BIBREF22 to the rows of $\mathbf {\hat{H}}^T$  As shown in Eq  DISPLAY_FORM17, $\mathbf {\hat{H}}^T$ is first passed to a dense layer parameterized by matrix $\mathbf {W}_A^T \in \mathbb {R}^{d \times d}$  An alignment vector $\mathbf {a}$ is then derived by comparing, via dot products, the rows of the output of the dense layer $\mathbf {Y}^T \in \mathbb {R}^{(n-1) \times d}$ with a trainable vector $\mathbf {v}^T \in \mathbb {R}^d$ (initialized randomly) and normalizing with a softmax  The normalized alignment coefficients are finally used to compute the attentional vector $\mathbf {u}^T \in \mathbb {R}^d$ as a weighted sum of the final representations $\mathbf {\hat{H}}^T$ 
Note that we tried with multiple context vectors, i e , with a matrix $\mathbf {V}^T$ instead of a vector $\mathbf {v}^T$, like in BIBREF22, but results were not convincing, even when adding a regularization term to the loss to favor diversity among the rows of $\mathbf {V}^T$ 
Master node skip connection  $\mathbf {h}_G^T \in \mathbb {R}^{2d}$ is obtained by concatenating $\mathbf {u}^T$ and the final master node representation  That is, the master node vector bypasses the attention mechanism  This is equivalent to a skip or shortcut connection BIBREF23  The reason behind this choice is that we expect the special document node to learn a high-level summary about the document, such as its size, vocabulary, etc  (more details are given in subsection SECREF30)  Therefore, by making the master node bypass the attention layer, we directly inject global information about the document into its final representation 
Multi-readout  BIBREF14, inspired by Jumping Knowledge Networks BIBREF12, recommend to not only use the final representations when performing readout, but also that of the earlier steps  Indeed, as one iterates, node features capture more and more global information  However, retaining more local, intermediary information might be useful too  Thus, instead of applying the readout function only to $t=T$, we apply it to all time steps and concatenate the results, finally obtaining $\mathbf {h}_G \in \mathbb {R}^{T \times 2d}$ :
In effect, with this modification, we take into account features based on information aggregated from subtrees of different heights (from 1 to $T$), corresponding to local and global features 
Message Passing Attention network for Document understanding (MPAD) ::: Hierarchical variants of MPAD
Through the successive MP iterations, it could be argued that MPAD implicitly captures some soft notion of the hierarchical structure of documents (words $\rightarrow $ bigrams $\rightarrow $ compositions of bigrams, etc )  However, it might be beneficial to explicitly capture document hierarchy  Hierarchical architectures have brought significant improvements to many NLP tasks, such as language modeling and generation BIBREF24, BIBREF25, sentiment and topic classification BIBREF26, BIBREF27, and spoken language understanding BIBREF28, BIBREF29  Inspired by this line of research, we propose several hierarchical variants of MPAD, detailed in what follows  In all of them, we represent each sentence in the document as a word co-occurrence network, and obtain an embedding for it by applying MPAD as previously described 
MPAD-sentence-att  Here, the sentence embeddings are simply combined through self-attention 
MPAD-clique  In this variant, we build a complete graph where each node represents a sentence  We then feed that graph to MPAD, where the feature vectors of the nodes are initialized with the sentence embeddings previously obtained 
MPAD-path  This variant is similar to the clique one, except that instead of a complete graph, we build a path according to the natural flow of the text  That is, two nodes are linked by a directed edge if the two sentences they represent follow each other in the document 
Experiments ::: Datasets
We evaluate the quality of the document embeddings learned by MPAD on 10 document classification datasets, covering the topic identification, coarse and fine sentiment analysis and opinion mining, and subjectivity detection tasks  We briefly introduce the datasets next  Their statistics are reported in Table TABREF21 
(1) Reuters  This dataset contains stories collected from the Reuters news agency in 1987  Following common practice, we used the ModApte split and considered only the 10 classes with the highest number of positive training examples  We also removed documents belonging to more than one class and then classes left with no document (2 classes) 
(2) BBCSport BIBREF30 contains documents from the BBC Sport website corresponding to 2004-2005 sports news articles 
(3) Polarity BIBREF31 features positive and negative labeled snippets from Rotten Tomatoes 
(4) Subjectivity BIBREF32 contains movie review snippets from Rotten Tomatoes (subjective sentences), and Internet Movie Database plot summaries (objective sentences) 
(5) MPQA BIBREF33 is made of positive and negative phrases, annotated as part of the summer 2002 NRRC Workshop on Multi-Perspective Question Answering 
(6) IMDB BIBREF34 is a collection of highly polarized movie reviews from IMDB (positive and negative)  There are at most 30 reviews for each movie 
(7) TREC BIBREF35 consists of questions that are classified into 6 different categories 
(8) SST-1 BIBREF36 contains the same snippets as Polarity  The authors used the Stanford Parser to parse the snippets and split them into multiple sentences  They then used Amazon Mechanical Turk to annotate the resulting phrases according to their polarity (very negative, negative, neutral, positive, very positive) 
(9) SST-2 BIBREF36 is the same as SST-1 but with neutral reviews removed and snippets classified as positive or negative 
(10) Yelp2013 BIBREF26 features reviews obtained from the 2013 Yelp Dataset Challenge 
Experiments ::: Baselines
We evaluate MPAD against multiple state-of-the-art baseline models, including hierarchical ones, to enable fair comparison with the hierarchical MPAD variants 
doc2vec BIBREF37  Doc2vec (or paragraph vector) is an extension of word2vec that learns vectors for documents in a fully unsupervised manner  Document embeddings are then fed to a logistic regression classifier 
CNN BIBREF38  The convolutional neural network architecture, well-known in computer vision, is applied to text

ground_truth: LSA, TextRank, LexRank and ILP-based summary.

LSA, TextRank, LexRank

prompt_front: You are given a scientific article and a question. Answer the question as concisely as you can, using a single phrase or sentence if possible. If the question cannot be answered based on the information in the article, write "unanswerable". If the question is a yes/no question, answer "yes", "no", or "unanswerable". Do not provide any explanation.

Please answer the questions based on the text provided below. Which component is the least impactful?
Context 3:  There is one spatial dimension and the word embeddings are used as channels (depth dimensions) 
DAN BIBREF39  The Deep Averaging Network passes the unweighted average of the embeddings of the input words through multiple dense layers and a final softmax 
Tree-LSTM BIBREF40 is a generalization of the standard LSTM architecture to constituency and dependency parse trees 
DRNN BIBREF41  Recursive neural networks are stacked and applied to parse trees 
LSTMN BIBREF42 is an extension of the LSTM model where the memory cell is replaced by a memory network which stores word representations 
C-LSTM BIBREF43 combines convolutional and recurrent neural networks  The region embeddings provided by a CNN are fed to a LSTM 
SPGK BIBREF44 also models documents as word co-occurrence networks  It computes a graph kernel that compares shortest paths extracted from the word co-occurrence networks and then uses a SVM to categorize documents 
WMD BIBREF45 is an application of the well-known Earth Mover's Distance to text  A k-nearest neighbor classifier is used 
S-WMD BIBREF46 is a supervised extension of the Word Mover's Distance 
Semantic-CNN BIBREF47  Here, a CNN is applied to semantic units obtained by clustering words in the embedding space 
LSTM-GRNN BIBREF26 is a hierarchical model where sentence embeddings are obtained with a CNN and a GRU-RNN is fed the sentence representations to obtain a document vector 
HN-ATT BIBREF27 is another hierarchical model, where the same encoder architecture (a bidirectional GRU-RNN) is used for both sentences and documents, with different parameters  A self-attention mechanism is applied to the RNN annotations at each level 
Experiments ::: Model configuration and training
We preprocess all datasets using the code of BIBREF38  On Yelp2013, we also replace all tokens appearing strictly less than 6 times with a special UNK token, like in BIBREF27  We then build a directed word co-occurrence network from each document, with a window of size 2 
We use two MP iterations ($T$=2) for the basic MPAD, and two MP iterations at each level, for the hierarchical variants  We set $d$ to 64, except on IMDB and Yelp on which $d=128$, and use a two-layer MLP  The final graph representations are passed through a softmax for classification  We train MPAD in an end-to-end fashion by minimizing the cross-entropy loss function with the Adam optimizer BIBREF48 and an initial learning rate of 0 001 
To regulate potential differences in magnitude, we apply batch normalization after concatenating the feature vector of the master node with the self-attentional vector, that is, after the skip connection (see subsection SECREF16)  To prevent overfitting, we use dropout BIBREF49 with a rate of 0 5  We select the best epoch, capped at 200, based on the validation accuracy  When cross-validation is used (see 3rd column of Table TABREF21), we construct a validation set by randomly sampling 10% of the training set of each fold 
On all datasets except Yelp2013, we use the publicly available 300-dimensional pre-trained Google News vectors ($D$=300) BIBREF50 to initialize the node representations $\mathbf {H}^0$  On Yelp2013, we follow BIBREF27 and learn our own word vectors from the training and validation sets with the gensim implementation of word2vec BIBREF51 
MPAD was implemented in Python 3 6 using the PyTorch library BIBREF52  All experiments were run on a single machine consisting of a 3 4 GHz Intel Core i7 CPU with 16 GB of RAM and an NVidia GeForce Titan Xp GPU 
Results and ablations ::: Results
Experimental results are shown in Table TABREF28  For the baselines, the best scores reported in each original paper are shown  MPAD reaches best performance on 7 out of 10 datasets, and is close second elsewhere  Moreover, the 7 datasets on which MPAD ranks first widely differ in training set size, number of categories, and prediction task (topic, sentiment, subjectivity), which indicates that MPAD can perform well in different settings 
MPAD vs  hierarchical variants  On 9 datasets out of 10, one or more of the hierarchical variants outperform the vanilla MPAD architecture, highlighting the benefit of explicitly modeling the hierarchical nature of documents 
However, on Subjectivity, standard MPAD outperforms all hierarchical variants  On TREC, it reaches the same accuracy  We hypothesize that in some cases, using a different graph to separately encode each sentence might be worse than using one single graph to directly encode the document  Indeed, in the single document graph, some words that never appear in the same sentence can be connected through common neighbors, as was explained in subsection SECREF7  So, this way, some notion of cross-sentence context is captured while learning representations of words, bigrams, etc  at each MP iteration  This creates better informed representations, resulting in a better document embedding  With the hierarchical variants, on the other hand, each sentence vector is produced in isolation, without any contextual information about the other sentences in the document  Therefore, the final sentence embeddings might be of lower quality, and as a group might also contain redundant/repeated information  When the sentence vectors are finally combined into a document representation, it is too late to take context into account 
Results and ablations ::: Ablation studies
To understand the impact of some hyperparameters on performance, we conducted additional experiments on the Reuters, Polarity, and IMDB datasets, with the non-hierarchical version of MPAD  Results are shown in Table TABREF29 
Number of MP iterations  First, we varied the number of message passing iterations from 1 to 4  We can clearly see in Table TABREF29 that having more iterations improves performance  We attribute this to the fact that we are reading out at each iteration from 1 to $T$ (see Eq  DISPLAY_FORM18), which enables the final graph representation to encode a mixture of low-level and high-level features  Indeed, in initial experiments involving readout at $t$=$T$ only, setting $T\ge 2$ was always decreasing performance, despite the GRU-based updates (Eq  DISPLAY_FORM14)  These results were consistent with that of BIBREF53 and BIBREF9, who both are reading out only at $t$=$T$ too  We hypothesize that node features at $T\ge 2$ are too diffuse to be entirely relied upon during readout  More precisely, initially at $t$=0, node representations capture information about words, at $t$=1, about their 1-hop neighborhood (bigrams), at $t$=2, about compositions of bigrams, etc  Thus, pretty quickly, node features become general and diffuse  In such cases, considering also the lower-level, more precise features of the earlier iterations when reading out may be necessary 
Undirected edges  On Reuters, using an undirected graph leads to better performance, while on Polarity and IMDB, it is the opposite  This can be explained by the fact that Reuters is a topic classification task, for which the presence or absence of some patterns is important, but not necessarily the order in which they appear, while Polarity and IMDB are sentiment analysis tasks  To capture sentiment, modeling word order is crucial, e g , in detecting negation 
No master node  Removing the master node deteriorates performance across all datasets, clearly showing the value of having such a node  We hypothesize that since the special document node is connected to all other nodes, it is able to encode during message passing a summary of the document 
No renormalization  Here, we do not use the renormalization trick of BIBREF9 during MP (see subsection SECREF10)  That is, Eq  DISPLAY_FORM11 becomes $\mathbf {M}^{t+1} = \textsc {MLP}^{t+1}\big (\mathbf {A}\mathbf {H}^{t}\big )$  In other words, instead of computing a weighted average of the incoming neighbors' feature vectors, we compute a weighted sum of them  Unlike the mean, which captures distributions, the sum captures structural information BIBREF14  As shown in Table TABREF29, using sum instead of mean decreases performance everywhere, suggesting that in our application, capturing the distribution of neighbor representations is more important that capturing their structure  We hypothesize that this is the case because statistical word co-occurrence networks tend to have similar structural properties, regardless of the topic, polarity, sentiment, etc  of the corresponding documents 
Neighbors-only  In this experiment, we replaced the GRU combine function (see Eq  DISPLAY_FORM14) with the identity function  That is, we simply have $\mathbf {H}^{t+1}$=$\mathbf {M}^{t+1}$  Since $\mathbf {A}$ has zero diagonal, by doing so, we completely ignore the previous feature of the node itself when updating its representation  That is, the update is based entirely on its neighbors  Except on Reuters (almost no change), performance always suffers, stressing the need to take into account the root node during updates, not only its neighborhood 
Related work
In what follows, we offer a brief review of relevant studies, ranked by increasing order of similarity with our work 
BIBREF9, BIBREF54, BIBREF11, BIBREF10 conduct some node classification experiments on citation networks, where nodes are scientific papers, i e , textual data  However, text is only used to derive node feature vectors  The external graph structure, which plays a central role in determining node labels, is completely unrelated to text 
On the other hand, BIBREF55, BIBREF7 experiment on traditional document classification tasks  They both build $k$-nearest neighbor similarity graphs based on the Gaussian diffusion kernel  More precisely, BIBREF55 build one single graph where nodes are documents and distance is computed in the BoW space  Node features are then used for classification  Closer to our work, BIBREF7 represent each document as a graph  All document graphs are derived from the same underlying structure  Only node features, corresponding to the entries of the documents' BoW vectors, vary  The underlying, shared structure is that of a $k$-NN graph where nodes are vocabulary terms and similarity is the cosine of the word embedding vectors  BIBREF7 then perform graph classification  However they found performance to be lower than that of a naive Bayes classifier 
BIBREF56 use a GNN for hierarchical classification into a large taxonomy of topics  This task differs from traditional document classification  The authors represent documents as unweighted, undirected word co-occurrence networks with word embeddings as node features  They then use the spatial GNN of BIBREF15 to perform graph classification 
The work closest to ours is probably that of BIBREF53  The authors adopt the semi-supervised node classification approach of BIBREF9  They build one single undirected graph from the entire dataset, with both word and document nodes  Document-word edges are weighted by TF-IDF and word-word edges are weighted by pointwise mutual information derived from co-occurrence within a sliding window  There are no document-document edges  The GNN is trained based on the cross-entropy loss computed only for the labeled nodes, that is, the documents in the training set  When the final node representations are obtained, one can use that of the test documents to classify them and evaluate prediction performance 
There are significant differences between BIBREF53 and our work  First, our approach is inductive, not transductive  Indeed, while the node classification approach of BIBREF53 requires all test documents at training time, our graph classification model is able to perform inference on new, never-seen documents  The downside of representing documents as separate graphs, however, is that we lose the ability to capture corpus-level dependencies  Also, our directed graphs capture word ordering, which is ignored by BIBREF53  Finally, the approach of BIBREF53 requires computing the PMI for every word pair in the vocabulary, which may be prohibitive on datasets with very large vocabularies  On the other hand, the complexity of MPAD does not depend on vocabulary size 
Conclusion
We have proposed an application of the message passing framework to NLP, the Message Passing Attention network for Document understanding (MPAD)  Experiments conducted on 10 standard text classification datasets show that our architecture is competitive with the state-of-the-art  By processing weighted, directed word co-occurrence networks, MPAD is sensitive to word order and word-word relationship strength  To explicitly capture the hierarchical structure of documents, we also propose three hierarchical variants of MPAD, that we show bring improvements over the vanilla architecture 
Acknowledgments
We thank the NVidia corporation for the donation of a GPU as part of their GPU grant program 

ground_truth: Based on table results provided changing directed to undirected edges had least impact - max abs difference of 0.33 points on all three datasets.