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. Ngrams of which length are aligned using PARENT?
Context 1: Introduction
The task of generating natural language descriptions of structured data (such as tables) BIBREF2 , BIBREF3 , BIBREF4 has seen a growth in interest with the rise of sequence to sequence models that provide an easy way of encoding tables and generating text from them BIBREF0 , BIBREF1 , BIBREF5 , BIBREF6  
For text generation tasks, the only gold standard metric is to show the output to humans for judging its quality, but this is too expensive to apply repeatedly anytime small modifications are made to a system  Hence, automatic metrics that compare the generated text to one or more reference texts are routinely used to compare models BIBREF7   For table-to-text generation, automatic evaluation has largely relied on BLEU BIBREF8 and ROUGE BIBREF9   The underlying assumption behind these metrics is that the reference text is gold-standard, i e , it is the ideal target text that a system should generate  In practice, however, when datasets are collected automatically and heuristically, the reference texts are often not ideal  Figure FIGREF2 shows an example from the WikiBio dataset BIBREF0   Here the reference contains extra information which no system can be expected to produce given only the associated table  We call such reference texts divergent from the table 
We show that existing automatic metrics, including BLEU, correlate poorly with human judgments when the evaluation sets contain divergent references (§ SECREF36 )  For many table-to-text generation tasks, the tables themselves are in a pseudo-natural language format (e g , WikiBio, WebNLG BIBREF6 , and E2E-NLG BIBREF10 )  In such cases we propose to compare the generated text to the underlying table as well to improve evaluation  We develop a new metric, PARENT (Precision And Recall of Entailed N-grams from the Table) (§ SECREF3 )  When computing precision, PARENT effectively uses a union of the reference and the table, to reward correct information missing from the reference  When computing recall, it uses an intersection of the reference and the table, to ignore extra incorrect information in the reference  The union and intersection are computed with the help of an entailment model to decide if a text n-gram is entailed by the table  We show that this method is more effective than using the table as an additional reference  Our main contributions are:
Table-to-Text Generation
We briefly review the task of generating natural language descriptions of semi-structured data, which we refer to as tables henceforth BIBREF11 , BIBREF12   Tables can be expressed as set of records INLINEFORM0 , where each record is a tuple (entity, attribute, value)  When all the records are about the same entity, we can truncate the records to (attribute, value) pairs  For example, for the table in Figure FIGREF2 , the records are {(Birth Name, Michael Dahlquist), (Born, December 22 1965),    }  The task is to generate a text INLINEFORM1 which summarizes the records in a fluent and grammatical manner  For training and evaluation we further assume that we have a reference description INLINEFORM2 available for each table  We let INLINEFORM3 denote an evaluation set of tables, references and texts generated from a model INLINEFORM4 , and INLINEFORM5 , INLINEFORM6 denote the collection of n-grams of order INLINEFORM7 in INLINEFORM8 and INLINEFORM9 , respectively  We use INLINEFORM10 to denote the count of n-gram INLINEFORM11 in INLINEFORM12 , and INLINEFORM13 to denote the minimum of its counts in INLINEFORM14 and INLINEFORM15   Our goal is to assign a score to the model, which correlates highly with human judgments of the quality of that model 
PARENT
PARENT evaluates each instance INLINEFORM0 separately, by computing the precision and recall of INLINEFORM1 against both INLINEFORM2 and INLINEFORM3  
Evaluation via Information Extraction
BIBREF1 proposed to use an auxiliary model, trained to extract structured records from text, for evaluation  However, the extraction model presented in that work is limited to the closed-domain setting of basketball game tables and summaries  In particular, they assume that each table has exactly the same set of attributes for each entity, and that the entities can be identified in the text via string matching  These assumptions are not valid for the open-domain WikiBio dataset, and hence we train our own extraction model to replicate their evaluation scheme 
Our extraction system is a pointer-generator network BIBREF19 , which learns to produce a linearized version of the table from the text  The network learns which attributes need to be populated in the output table, along with their values  It is trained on the training set of WikiBio  At test time we parsed the output strings into a set of (attribute, value) tuples and compare it to the ground truth table  The F-score of this text-to-table system was INLINEFORM0 , which is comparable to other challenging open-domain settings BIBREF20   More details are included in the Appendix SECREF52  
Given this information extraction system, we consider the following metrics for evaluation, along the lines of BIBREF1   Content Selection (CS): F-score for the (attribute, value) pairs extracted from the generated text compared to those extracted from the reference  Relation Generation (RG): Precision for the (attribute, value) pairs extracted from the generated text compared to those in the ground truth table  RG-F: Since our task emphasizes the recall of information from the table as well, we consider another variant which computes the F-score of the extracted pairs to those in the table  We omit the content ordering metric, since our extraction system does not align records to the input text 
Experiments & Results
In this section we compare several automatic evaluation metrics by checking their correlation with the scores assigned by humans to table-to-text models  Specifically, given INLINEFORM0 models INLINEFORM1 , and their outputs on an evaluation set, we show these generated texts to humans to judge their quality, and obtain aggregated human evaluation scores for all the models, INLINEFORM2 (§ SECREF33 )  Next, to evaluate an automatic metric, we compute the scores it assigns to each model, INLINEFORM3 , and check the Pearson correlation between INLINEFORM4 and INLINEFORM5 BIBREF21  
Data & Models
Our main experiments are on the WikiBio dataset BIBREF0 , which is automatically constructed and contains many divergent references  In § SECREF47 we also present results on the data released as part of the WebNLG challenge 
We developed several models of varying quality for generating text from the tables in WikiBio  This gives us a diverse set of outputs to evaluate the automatic metrics on  Table TABREF32 lists the models along with their hyperparameter settings and their scores from the human evaluation (§ SECREF33 )  Our focus is primarily on neural sequence-to-sequence methods since these are most widely used, but we also include a template-based baseline  All neural models were trained on the WikiBio training set  Training details and sample outputs are included in Appendices SECREF56 & SECREF57  
We divide these models into two categories and measure correlation separately for both the categories  The first category, WikiBio-Systems, includes one model each from the four families listed in Table TABREF32   This category tests whether a metric can be used to compare different model families with a large variation in the quality of their outputs  The second category, WikiBio-Hyperparams, includes 13 different hyperparameter settings of PG-Net BIBREF19 , which was the best performing system overall  9 of these were obtained by varying the beam size and length normalization penalty of the decoder network BIBREF23 , and the remaining 4 were obtained by re-scoring beams of size 8 with the information extraction model described in § SECREF4   All the models in this category produce high quality fluent texts, and differ primarily on the quantity and accuracy of the information they express  Here we are testing whether a metric can be used to compare similar systems with a small variation in performance  This is an important use-case as metrics are often used to tune hyperparameters of a model 
Human Evaluation
We collected human judgments on the quality of the 16 models trained for WikiBio, plus the reference texts  Workers on a crowd-sourcing platform, proficient in English, were shown a table with pairs of generated texts, or a generated text and the reference, and asked to select the one they prefer  Figure FIGREF34 shows the instructions they were given  Paired comparisons have been shown to be superior to rating scales for comparing generated texts BIBREF24   However, for measuring correlation the comparisons need to be aggregated into real-valued scores, INLINEFORM0 , for each of the INLINEFORM1 models  For this, we use Thurstone's method BIBREF22 , which assigns a score to each model based on how many times it was preferred over an alternative 
The data collection was performed separately for models in the WikiBio-Systems and WikiBio-Hyperparams categories  1100 tables were sampled from the development set, and for each table we got 8 different sentence pairs annotated across the two categories, resulting in a total of 8800 pairwise comparisons  Each pair was judged by one worker only which means there may be noise at the instance-level, but the aggregated system-level scores had low variance (cf  Table TABREF32 )  In total around 500 different workers were involved in the annotation  References were also included in the evaluation, and they received a lower score than PG-Net, highlighting the divergence in WikiBio 
Compared Metrics
Text only: We compare BLEU BIBREF8 , ROUGE BIBREF9 , METEOR BIBREF18 , CIDEr and CIDEr-D BIBREF25 using their publicly available implementations 
Information Extraction based: We compare the CS, RG and RG-F metrics discussed in § SECREF4  
Text & Table: We compare a variant of BLEU, denoted as BLEU-T, where the values from the table are used as additional references  BLEU-T draws inspiration from iBLEU BIBREF26 but instead rewards n-grams which match the table rather than penalizing them  For PARENT, we compare both the word-overlap model (PARENT-W) and the co-occurrence model (PARENT-C) for determining entailment  We also compare versions where a single INLINEFORM0 is tuned on the entire dataset to maximize correlation with human judgments, denoted as PARENT*-W/C 
Correlation Comparison
We use bootstrap sampling (500 iterations) over the 1100 tables for which we collected human annotations to get an idea of how the correlation of each metric varies with the underlying data  In each iteration, we sample with replacement, tables along with their references and all the generated texts for that table  Then we compute aggregated human evaluation and metric scores for each of the models and compute the correlation between the two  We report the average correlation across all bootstrap samples for each metric in Table TABREF37   The distribution of correlations for the best performing metrics are shown in Figure FIGREF38  
Table TABREF37 also indicates whether PARENT is significantly better than a baseline metric  BIBREF21 suggest using the William's test for this purpose, but since we are computing correlations between only 4/13 systems at a time, this test has very weak power in our case  Hence, we use the bootstrap samples to obtain a INLINEFORM0 confidence interval of the difference in correlation between PARENT and any other metric and check whether this is above 0 BIBREF27  
Correlations are higher for the systems category than the hyperparams category  The latter is a more difficult setting since very similar models are compared, and hence the variance of the correlations is also high  Commonly used metrics which only rely on the reference (BLEU, ROUGE, METEOR, CIDEr) have only weak correlations with human judgments  In the hyperparams category, these are often negative, implying that tuning models based on these may lead to selecting worse models  BLEU performs the best among these, and adding n-grams from the table as references improves this further (BLEU-T) 
Among the extractive evaluation metrics, CS, which also only relies on the reference, has poor correlation in the hyperparams category  RG-F, and both variants of the PARENT metric achieve the highest correlation for both settings  There is no significant difference among these for the hyperparams category, but for systems, PARENT-W is significantly better than the other two  While RG-F needs a full information extraction pipeline in its implementation, PARENT-C only relies on co-occurrence counts, and PARENT-W can be used out-of-the-box for any dataset  To our knowledge, this is the first rigorous evaluation of using information extraction for generation evaluation 
On this dataset, the word-overlap model showed higher correlation than the co-occurrence model for entailment  In § SECREF47 we will show that for the WebNLG dataset, where more paraphrasing is involved between the table and text, the opposite is true  Lastly, we note that the heuristic for selecting INLINEFORM0 is sufficient to produce high correlations for PARENT, however, if human annotations are available, this can be tuned to produce significantly higher correlations (PARENT*-W/C) 
Analysis
In this section we further analyze the performance of PARENT-W under different conditions, and compare to the other best metrics from Table TABREF37  
To study the correlation as we vary the number of divergent references, we also collected binary labels from workers for whether a reference is entailed by the corresponding table  We define a reference as entailed when it mentions only information which can be inferred from the table  Each table and reference pair was judged by 3 independent workers, and we used the majority vote as the label for that pair  Overall, only INLINEFORM0 of the references were labeled as entailed by the table  Fleiss' INLINEFORM1 was INLINEFORM2 , which indicates a fair agreement  We found the workers sometimes disagreed on what information can be reasonably entailed by the table 
Figure FIGREF40 shows the correlations as we vary the percent of entailed examples in the evaluation set of WikiBio  Each point is obtained by fixing the desired proportion of entailed examples, and sampling subsets from the full set which satisfy this proportion  PARENT and RG-F remain stable and show a high correlation across the entire range, whereas BLEU and BLEU-T vary a lot  In the hyperparams category, the latter two have the worst correlation when the evaluation set contains only entailed examples, which may seem surprising  However, on closer examination we found that this subset tends to omit a lot of information from the tables  Systems which produce more information than these references are penalized by BLEU, but not in the human evaluation  PARENT overcomes this issue by measuring recall against the table in addition to the reference 
We check how different components in the computation of PARENT contribute to its correlation to human judgments  Specifically, we remove the probability INLINEFORM0 of an n-gram INLINEFORM1 being entailed by the table from Eqs  EQREF19 and EQREF23   The average correlation for PARENT-W drops to INLINEFORM5 in this case  We also try a variant of PARENT with INLINEFORM6 , which removes the contribution of Table Recall (Eq  EQREF22 )  The average correlation is INLINEFORM7 in this case  With these components, the correlation is INLINEFORM8 , showing that they are crucial to the performance of PARENT 
BIBREF28 point out that hill-climbing on an automatic metric is meaningless if that metric has a low instance-level correlation to human judgments  In Table TABREF46 we show the average accuracy of the metrics in making the same judgments as humans between pairs of generated texts  Both variants of PARENT are significantly better than the other metrics, however the best accuracy is only INLINEFORM0 for the binary task  This is a challenging task, since there are typically only subtle differences between the texts  Achieving higher instance-level accuracies will require more sophisticated language understanding models for evaluation 
WebNLG Dataset
To check how PARENT correlates with human judgments when the references are elicited from humans (and less likely to be divergent), we check its correlation with the human ratings provided for the systems competing in the WebNLG challenge BIBREF6   The task is to generate text describing 1-5 RDF triples (e g  John E Blaha, birthPlace, San Antonio), and human ratings were collected for the outputs of 9 participating systems on 223 instances  These systems include a mix of pipelined, statistical and neural methods  Each instance has upto 3 reference texts associated with the RDF triples, which we use for evaluation 
The human ratings were collected on 3 distinct aspects – grammaticality, fluency and semantics, where semantics corresponds to the degree to which a generated text agrees with the meaning of the underlying RDF triples  We report the correlation of several metrics with these ratings in Table TABREF48   Both variants of PARENT are either competitive or better than the other metrics in terms of the average correlation to all three aspects  This shows that PARENT is applicable for high quality references as well 
While BLEU has the highest correlation for the grammar and fluency aspects, PARENT does best for semantics  This suggests that the inclusion of source tables into the evaluation orients the metric more towards measuring the fidelity of the content of the generation  A similar trend is seen comparing BLEU and BLEU-T  As modern neural text generation systems are typically very fluent, measuring their fidelity is of increasing importance  Between the two entailment models, PARENT-C is better due to its higher correlation with the grammaticality and fluency aspects 
The INLINEFORM0 parameter in the calculation of PARENT decides whether to compute recall against the table or the reference (Eq  EQREF22 )  Figure FIGREF50 shows the distribution of the values taken by INLINEFORM1 using the heuristic described in § SECREF3 for instances in both WikiBio and WebNLG  For WikiBio, the recall of the references against the table is generally low, and hence the recall of the generated text relies more on the table  For WebNLG, where the references are elicited from humans, this recall is much higher (often INLINEFORM2 ), and hence the recall of the generated text relies more on the reference 
Related Work
Over the years several studies have evaluated automatic metrics for measuring text generation performance BIBREF29 , BIBREF30 , BIBREF31 , BIBREF32 , BIBREF33 , BIBREF34 , BIBREF35   The only consensus from these studies seems to be that no single metric is suitable across all tasks  A recurring theme is that metrics like BLEU and NIST BIBREF36 are not suitable for judging content quality in NLG  Recently, BIBREF37 did a comprehensive study of several metrics on the outputs of state-of-the-art NLG systems, and found that while they showed acceptable correlation with human judgments at the system level, they failed to show any correlation at the sentence level  Ours is the first study which checks the quality of metrics when table-to-text references are divergent  We show that in this case even system level correlations can be unreliable 
Hallucination BIBREF38 , BIBREF39 refers to when an NLG system generates text which mentions extra information than what is present in the source from which it is generated  Divergence can be viewed as hallucination in the reference text itself  PARENT deals with hallucination by discounting n-grams which do not overlap with either the reference or the table 
PARENT draws inspiration from iBLEU BIBREF26 , a metric for evaluating paraphrase generation, which compares the generated text to both the source text and the reference  While iBLEU penalizes texts which match the source, here we reward such texts since our task values accuracy of generated text more than the need for paraphrasing the tabular content BIBREF40   Similar to SARI for text simplification BIBREF41 and Q-BLEU for question generation BIBREF42 , PARENT falls under the category of task-specific metrics 
Conclusions
We study the automatic evaluation of table-to-text systems when the references diverge from the table  We propose a new metric, PARENT, which shows the highest correlation with humans across a range of settings with divergent references in WikiBio  We also perform the first empirical evaluation of information extraction based metrics BIBREF1 , and find RG-F to be effective  Lastly, we show that PARENT is comparable to the best existing metrics when references are elicited by humans on the WebNLG data 
Acknowledgements
Bhuwan Dhingra is supported by a fellowship from Siemens, and by grants from Google  We thank Maruan Al-Shedivat, Ian Tenney, Tom Kwiatkowski, Michael Collins, Slav Petrov, Jason Baldridge, David Reitter and other members of the Google AI Language team for helpful discussions and suggestions  We thank Sam Wiseman for sharing data for an earlier version of this paper  We also thank the anonymous reviewers for their feedback 
Information Extraction System
For evaluation via information extraction BIBREF1 we train a model for WikiBio which accepts text as input and generates a table as the output  Tables in WikiBio are open-domain, without any fixed schema for which attributes may be present or absent in an instance  Hence we employ the Pointer-Generator Network (PG-Net) BIBREF19 for this purpose  Specifically, we use a sequence-to-sequence model, whose encoder and decoder are both single-layer bi-directional LSTMs  The decoder is augmented with an attention mechanism over the states of the encoder  Further, it also uses a copy mechanism to optionally copy tokens directly from the source text  We do not use the coverage mechanism of BIBREF19 since that is specific to the task of summarization they study  The decoder is trained to produce a linearized version of the table where the rows and columns are flattened into a sequence, and separate by special tokens  Figure FIGREF53 shows an example 
Clearly, since the references are divergent, the model cannot be expected to produce the entire table, and we see some false information being hallucinated after training  Nevertheless, as we show in § SECREF36 , this system can be used for evaluating generated texts  After training, we can parse the output sequence along the special tokens INLINEFORM0 R INLINEFORM1 and INLINEFORM2 C INLINEFORM3 to get a set of (attribute, value) pairs  Table TABREF54 shows the precision, recall and F-score of these extracted pairs against the ground truth tables, where the attributes and values are compared using an exact string match 
Hyperparameters
After tuning we found the same set of hyperparameters to work well for both the table-to-text PG-Net, and the inverse information extraction PG-Net  The hidden state size of the biLSTMs was set to 200  The input and output vocabularies were set to 50000 most common words in the corpus, with additional special symbols for table attribute names (such as “birth-date”)  The embeddings of the tokens in the vocabulary were initialized with Glove BIBREF43   Learning rate of INLINEFORM0 was used during training, with the Adam optimizer, and a dropout of INLINEFORM1 was also applied to the outputs of the biLSTM  Models were trained till the loss on the dev set stopped dropping  Maximum length of a decoded text was set to 40 tokens, and that of the tables was set to 120 tokens  Various beam sizes and length normalization penalties were applied for the table-to-text system, which are listed in the main paper  For the information extraction system, we found a beam size of 8 and no length penalty to produce the highest F-score on the dev set 
Sample Outputs
Table TABREF55 shows some sample references and the corresponding predictions from the best performing model, PG-Net for WikiBio 

ground_truth: Unanswerable

Answer with content missing: (Parent subsections) combine precisions for n-gram orders 1-4

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 background do they have?
Context 2: Introduction
In June 2015, the operators of the online discussion site Reddit banned several communities under new anti-harassment rules  BIBREF0 used this opportunity to combine rich online data with computational methods to study a current question: Does eliminating these “echo chambers” diminish the amount of hate speech overall? Exciting opportunities like these, at the intersection of “thick” cultural and societal questions on the one hand, and the computational analysis of rich textual data on larger-than-human scales on the other, are becoming increasingly common 
Indeed, computational analysis is opening new possibilities for exploring challenging questions at the heart of some of the most pressing contemporary cultural and social issues  While a human reader is better equipped to make logical inferences, resolve ambiguities, and apply cultural knowledge than a computer, human time and attention are limited  Moreover, many patterns are not obvious in any specific context, but only stand out in the aggregate  For example, in a landmark study, BIBREF1 analyzed the authorship of The Federalist Papers using a statistical text analysis by focusing on style, based on the distribution of function words, rather than content  As another example, BIBREF2 studied what defines English haiku and showed how computational analysis and close reading can complement each other  Computational approaches are valuable precisely because they help us identify patterns that would not otherwise be discernible 
Yet these approaches are not a panacea  Examining thick social and cultural questions using computational text analysis carries significant challenges  For one, texts are culturally and socially situated  They reflect the ideas, values and beliefs of both their authors and their target audiences, and such subtleties of meaning and interpretation are difficult to incorporate in computational approaches  For another, many of the social and cultural concepts we seek to examine are highly contested — hate speech is just one such example  Choices regarding how to operationalize and analyze these concepts can raise serious concerns about conceptual validity and may lead to shallow or obvious conclusions, rather than findings that reflect the depth of the questions we seek to address 
These are just a small sample of the many opportunities and challenges faced in computational analyses of textual data  New possibilities and frustrating obstacles emerge at every stage of research, from identification of the research question to interpretation of the results  In this article, we take the reader through a typical research process that involves measuring social or cultural concepts using computational methods, discussing both the opportunities and complications that often arise  In the Reddit case, for example, hate speech is measured, however imperfectly, by the presence of particular words semi-automatically extracted from a machine learning algorithm  Operationalizations are never perfect translations, and are often refined over the course of an investigation, but they are crucial 
We begin our exploration with the identification of research questions, proceed through data selection, conceptualization, and operationalization, and end with analysis and the interpretation of results  The research process sounds more or less linear this way, but each of these phases overlaps, and in some instances turns back upon itself  The analysis phase, for example, often feeds back into the original research questions, which may continue to evolve for much of the project  At each stage, our discussion is critically informed by insights from the humanities and social sciences, fields that have focused on, and worked to tackle, the challenges of textual analysis—albeit at smaller scales—since their inception 
In describing our experiences with computational text analysis, we hope to achieve three primary goals  First, we aim to shed light on thorny issues not always at the forefront of discussions about computational text analysis methods  Second, we hope to provide a set of best practices for working with thick social and cultural concepts  Our guidance is based on our own experiences and is therefore inherently imperfect  Still, given our diversity of disciplinary backgrounds and research practices, we hope to capture a range of ideas and identify commonalities that will resonate for many  And this leads to our final goal: to help promote interdisciplinary collaborations  Interdisciplinary insights and partnerships are essential for realizing the full potential of any computational text analysis that involves social and cultural concepts, and the more we are able to bridge these divides, the more fruitful we believe our work will be 
Research questions
We typically start by identifying the questions we wish to explore  Can text analysis provide a new perspective on a “big question” that has been attracting interest for years? Or can we raise new questions that have only recently emerged, for example about social media? For social scientists working in computational analysis, the questions are often grounded in theory, asking: How can we explain what we observe? These questions are also influenced by the availability and accessibility of data sources  For example, the choice to work with data from a particular social media platform may be partly determined by the fact that it is freely available, and this will in turn shape the kinds of questions that can be asked  A key output of this phase are the concepts to measure, for example: influence; copying and reproduction; the creation of patterns of language use; hate speech  Computational analysis of text motivated by these questions is insight driven: we aim to describe a phenomenon or explain how it came about  For example, what can we learn about how and why hate speech is used or how this changes over time? Is hate speech one thing, or does it comprise multiple forms of expression? Is there a clear boundary between hate speech and other types of speech, and what features make it more or less ambiguous? In these cases, it is critical to communicate high-level patterns in terms that are recognizable 
This contrasts with much of the work in computational text analysis, which tends to focus on automating tasks that humans perform inefficiently  These tasks range from core linguistically motivated tasks that constitute the backbone of natural language processing, such as part-of-speech tagging and parsing, to filtering spam and detecting sentiment  Many tasks are motivated by applications, for example to automatically block online trolls  Success, then, is often measured by performance, and communicating why a certain prediction was made—for example, why a document was labeled as positive sentiment, or why a word was classified as a noun—is less important than the accuracy of the prediction itself  The approaches we use and what we mean by `success' are thus guided by our research questions 
Domain experts and fellow researchers can provide feedback on questions and help with dynamically revising them  For example, they may say “we already think we know that”, “that's too naïve”, “that doesn't reflect social reality” (negative); “two major camps in the field would give different answers to that question” (neutral); “we tried to look at that back in the 1960s, but we didn't have the technology” (positive); and “that sounds like something that people who made that archive would love”, “that's a really fundamental question” (very positive) 
Sometimes we also hope to connect to multiple disciplines  For example, while focusing on the humanistic concerns of an archive, we could also ask social questions such as “is this archive more about collaborative processes, culture-building or norm creation?” or “how well does this archive reflect the society in which it is embedded?" BIBREF3 used quantitative methods to tell a story about Darwin's intellectual development—an essential biographical question for a key figure in the history of science  At the same time, their methods connected Darwin's development to the changing landscape of Victorian scientific culture, allowing them to contrast Darwin's “foraging” in the scientific literature of his time to the ways in which that literature was itself produced  Finally, their methods provided a case study, and validation of technical approaches, for cognitive scientists who are interested in how people explore and exploit sources of knowledge 
Questions about potential “dual use” may also arise  Returning to our introductory example, BIBREF0 started with a deceptively simple question: if an internet platform eliminates forums for hate speech, does this impact hate speech in other forums? The research was motivated by the belief that a rising tide of online hate speech was (and is) making the internet increasingly unfriendly for disempowered groups, including minorities, women, and LBGTQ individuals  Yet the possibility of dual use troubled the researchers from the onset  Could the methodology be adopted to target the speech of groups like Black Lives Matter? Could it be adopted by repressive governments to minimize online dissent? While these concerns remained, they concluded that hypothetical dual use scenarios did not outweigh the tangible contribution this research could offer towards making the online environment more equal and just 
Data
The next step involves deciding on the data sources, collecting and compiling the dataset, and inspecting its metadata 
Data acquisition
Many scholars in the humanities and the social sciences work with sources that are not available in digital form, and indeed may never be digitized  Others work with both analogue and digitized materials, and the increasing digitization of archives has opened opportunities to study these archives in new ways  We can go to the canonical archive or open up something that nobody has studied before  For example, we might focus on major historical moments (French Revolution, post-Milosevic Serbia) or critical epochs (Britain entering the Victorian era, the transition from Latin to proto-Romance)  Or, we could look for records of how people conducted science, wrote and consumed literature, and worked out their philosophies 
A growing number of researchers work with born-digital sources or data  Born-digital data, e g , from social media, generally do not involve direct elicitation from participants and therefore enable unobtrusive measurements BIBREF5 , BIBREF6   In contrast, methods like surveys sometimes elicit altered responses from participants, who might adapt their responses to what they think is expected  Moreover, born-digital data is often massive, enabling large-scale studies of language and behavior in a variety of social contexts 
Still, many scholars in the social sciences and humanities work with multiple data sources  The variety of sources typically used means that more than one data collection method is often required  For example, a project examining coverage of a UK General Election, could draw data from traditional media, web archives, Twitter and Facebook, campaign manifestos, etc  and might combine textual analysis of these materials with surveys, laboratory experiments, or field observations offline  In contrast, many computational studies based on born-digital data have focused on one specific source, such as Twitter 
The use of born-digital data raises ethical concerns  Although early studies often treated privacy as a binary construct, many now acknowledge its complexity BIBREF7   Conversations on private matters can be posted online, visible for all, but social norms regarding what should be considered public information may differ from the data's explicit visibility settings  Often no informed consent has been obtained, raising concerns and challenges regarding publishing content and potentially harmful secondary uses BIBREF8 , BIBREF4  
Recently, concerns about potential harms stemming from secondary uses have led a number of digital service providers to restrict access to born-digital data  Facebook and Twitter, for example, have reduced or eliminated public access to their application programming interfaces (APIs) and expressed hesitation about allowing academic researchers to use data from their platforms to examine certain sensitive or controversial topics  Despite the seeming abundance of born-digital data, we therefore cannot take its availability for granted 
Working with data that someone else has acquired presents additional problems related to provenance and contextualisation  It may not always be possible to determine the criteria applied during the creation process  For example, why were certain newspapers digitized but not others, and what does this say about the collection? Similar questions arise with the use of born-digital data  For instance, when using the Internet Archive’s Wayback Machine to gather data from archived web pages, we need to consider what pages were captured, which are likely missing, and why 
We must often repurpose born-digital data (e g , Twitter was not designed to measure public opinion), but data biases may lead to spurious results and limit justification for generalization  In particular, data collected via black box APIs designed for commercial, not research, purposes are likely to introduce biases into the inferences we draw, and the closed nature of these APIs means we rarely know what biases are introduced, let alone how severely they might impact our research BIBREF10   These, however, are not new problems  Historians, for example, have always understood that their sources were produced within particular contexts and for particular purposes, which are not always apparent to us 
Non-representative data can still be useful for making comparisons within a sample  In the introductory example on hate speech BIBREF0 , the Reddit forums do not present a comprehensive or balanced picture of hate speech: the writing is almost exclusively in English, the targets of hate speech are mainly restricted (e g , to black people, or women), and the population of writers is shaped by Reddit's demographics, which skew towards young white men  These biases limit the generalizability of the findings, which cannot be extrapolated to other languages, other types of hate speech, and other demographic groups  However, because the findings are based on measurements on the same sort of hate speech and the same population of writers, as long as the collected data are representative of this specific population, these biases do not pose an intractable validity problem if claims are properly restricted 
The size of many newly available datasets is one of their most appealing characteristics  Bigger datasets often make statistics more robust  The size needed for a computational text analysis depends on the research goal: When it involves studying rare events, bigger datasets are needed  However, larger is not always better  Some very large archives are “secretly” collections of multiple and distinct processes that no in-field scholar would consider related  For example, Google Books is frequently used to study cultural patterns, but the over-representation of scientific articles in Google books can be problematic BIBREF11   Even very large born-digital datasets usually cover limited timespans compared to, e g , the Gutenberg archive of British novels 
This stage of the research also raises important questions about fairness  Are marginalized groups, for example, represented in the tweets we have collected? If not, what types of biases might result from analyses relying on those tweets?
Local experts and “informants” can help navigate the data  They can help understand the role an archive plays in the time and place  They might tell us: Is this the central archive, or a peripheral one? What makes it unusual? Or they might tell us how certain underrepresented communities use a social media platform and advise us on strategies for ensuring our data collection includes their perspectives 
However, when it is practically infeasible to navigate the data in this way—for instance, when we cannot determine what is missing from Twitter's Streaming API or what webpages are left out of the Internet Archive—we should be open about the limitations of our analyses, acknowledging the flaws in our data and drawing cautious and reasonable conclusions from them  In all cases, we should report the choices we have made when creating or re-using any dataset 
Compiling data
After identifying the data source(s), the next step is compiling the data  This step is fundamental: if the sources cannot support a convincing result, no result will be convincing  In many cases, this involves defining a “core" set of documents and a “comparison" set  We often have a specific set of documents in mind: an author's work, a particular journal, a time period  But if we want to say that this “core" set has some distinctive property, we need a “comparison" set  Expanding the collection beyond the documents that we would immediately think of has the beneficial effect of increasing our sample size  Having more sources increases the chance that we will notice something consistent across many individually varying contexts 
Comparing sets of documents can sometimes support causal inference, presented as a contrast between a treatment group and a control  In BIBREF0 , the treatment consisted of the text written in the two forums that were eventually closed by Reddit  However, identifying a control group required a considerable amount of time and effort  Reddit is a diverse platform, with a wide variety of interactional and linguistic styles; it would be pointless to compare hate speech forums against forums dedicated to, say, pictures of wrecked bicycles  Chandrasekharan et al  used a matching design, populating the control group with forums that were as similar as possible to the treatment group, but were not banned from Reddit  The goal is to estimate the counterfactual scenario: in this case, what would have happened had the site not taken action against these specific forums? An ideal control would make it possible to distinguish the effect of the treatment — closing the forums — from other idiosyncratic properties of texts that were treated 
We also look for categories of documents that might not be useful  We might remove documents that are meta-discourse, like introductions and notes, or documents that are in a language that is not the primary language of the collection, or duplicates when we are working with archived web pages  However, we need to carefully consider the potential consequences of information we remove  Does its removal alter the data, or the interpretation of the data, we are analyzing? Are we losing anything that might be valuable at a later stage?
Labels and metadata
Sometimes all we have is documents, but often we want to look at documents in the context of some additional information, or metadata  This additional information could tell us about the creation of documents (date, author, forum), or about the reception of documents (flagged as hate speech, helpful review)  Information about text segments can be extremely valuable, but it is also prone to errors, inconsistencies, bias, and missing information  Examining metadata is a good way to check a collection's balance and representativeness  Are sources disproportionately of one form? Is the collection missing a specific time window? This type of curation can be extremely time consuming as it may require expert labeling, but it often leads to the most compelling results  Sometimes metadata are also used as target labels to develop machine learning models  But using them as a “ground truth” requires caution  Labels sometimes mean something different than we expect  For example, a down vote for a social media post could indicate that the content is offensive, or that the voter simply disagreed with the expressed view 
Conceptualization
A core step in many analyses is translating social and cultural concepts (such as hate speech, rumor, or conversion) into measurable quantities  Before we can develop measurements for these concepts (the operationalization step, or the “implementation” step as denoted by BIBREF12 ), we need to define them  In the conceptualization phase we often start with questions such as: who are the domain experts, and how have they approached the topic? We are looking for a definition of the concept that is flexible enough to apply on our dataset, yet formal enough for computational research  For example, our introductory study on hate speech BIBREF0 used a statement on hate speech produced by the European Union Court of Human Rights  The goal was not to implement this definition directly in software but to use it as a reference point to anchor subsequent analyses 
If we want to move beyond the use of ad hoc definitions, it can be useful to distinguish between what political scientists Adcock and Collier call the “background concept” and the “systematized concept” BIBREF13   The background concept comprises the full and diverse set of meanings that might be associated with a particular term  This involves delving into theoretical, conceptual, and empirical studies to assess how a concept has been defined by other scholars and, most importantly, to determine which definition is most appropriate for the particular research question and the theoretical framework in which it is situated  That definition, in turn, represents the systematized concept: the formulation that is adopted for the study 
It is important to consider that for social and cultural concepts there is no absolute ground truth  There are often multiple valid definitions for a concept (the “background” concept in the terms of Adcock and Collier), and definitions might be contested over time  This may be uncomfortable for computer scientists, whose primary measure of success is often based on comparing a model's output against “ground truth” or a “gold standard”, e g , by comparing a sentiment classifier's output against manual annotations  However, the notion of ground truth is uncommon in the humanities and the social sciences and it is often taken too far in machine learning  BIBREF14 notes that in literary criticism and the digital humanities more broadly “interpretation, ambiguity, and argumentation are prized far above ground truth and definitive conclusions"  BIBREF15 draw attention to the different attitudes of literary scholars and computational linguists towards ambiguity, stating that “In Computational Linguistics [  ] ambiguity is almost uniformly treated as a problem to be solved; the focus is on disambiguation, with the assumption that one true, correct interpretation exists " The latter is probably true for tasks such as spam filtering, but in the social sciences and the humanities many relevant concepts are fundamentally unobservable, such as latent traits of political actors BIBREF16 or cultural fit in organizations BIBREF17 , leading to validation challenges  Moreover, when the ground truth comes from people, it may be influenced by ideological priors, priming, simple differences of opinion or perspective, and many other factors BIBREF18   We return to this issue in our discussions on validation and analysis 
Operationalization
In this phase we develop measures (or, “operationalizations”, or “indicators”) for the concepts of interest, a process called “operationalization”  Regardless of whether we are working with computers, the output produced coincides with Adcock and Collier's “scores”—the concrete translation and output of the systematized concept into numbers or labels BIBREF13   Choices made during this phase are always tied to the question “Are we measuring what we intend to measure?” Does our operationalization match our conceptual definition? To ensure validity we must recognize gaps between what is important and what is easy to measure  We first discuss modeling considerations  Next, we describe several frequently used computational approaches and their limitations and strengths 
Modeling considerations
The variables (both predictors and outcomes) are rarely simply binary or categorical  For example, a study on language use and age could focus on chronological age (instead of, e g , social age BIBREF19 )  However, even then, age can be modeled in different ways  Discretization can make the modeling easier and various NLP studies have modeled age as a categorical variable BIBREF20   But any discretization raises questions: How many categories? Where to place the boundaries? Fine distinctions might not always be meaningful for the analysis we are interested in, but categories that are too broad can threaten validity  Other interesting variables include time, space, and even the social network position of the author  It is often preferable to keep the variable in its most precise form  For example, BIBREF21 perform exploration in the context of hypothesis testing by using latitude and longitude coordinates — the original metadata attached to geotagged social media such as tweets — rather than aggregating into administrative units such as counties or cities  This is necessary when such administrative units are unlikely to be related to the target concept, as is the case in their analysis of dialect differences  Focusing on precise geographical coordinates also makes it possible to recognize fine-grained effects, such as language variation across the geography of a city 
Using a particular classification scheme means deciding which variations are visible, and which ones are hidden BIBREF22   We are looking for a categorization scheme for which it is feasible to collect a large enough labeled document collection (e g , to train supervised models), but which is also fine-grained enough for our purposes  Classification schemes rarely exhibit the ideal properties, i e , that they are consistent, their categories are mutually exclusive, and that the system is complete BIBREF22   Borderline cases are challenging, especially with social and cultural concepts, where the boundaries are often not clear-cut  The choice of scheme can also have ethical implications BIBREF22   For example, gender is usually represented as a binary variable in NLP and computational models tend to learn gender-stereotypical patterns  The operationalization of gender in NLP has been challenged only recently BIBREF23 , BIBREF24 , BIBREF25  
Supervised and unsupervised learning are the most common approaches to learning from data  With supervised learning, a model learns from labeled data (e g , social media messages labeled by sentiment) to infer (or predict) these labels from unlabeled texts  In contrast, unsupervised learning uses unlabeled data  Supervised approaches are especially suitable when we have a clear definition of the concept of interest and when labels are available (either annotated or native to the data)  Unsupervised approaches, such as topic models, are especially useful for exploration  In this setting, conceptualization and operationalization may occur simultaneously, with theory emerging from the data BIBREF26   Unsupervised approaches are also used when there is a clear way of measuring a concept, often based on strong assumptions  For example, BIBREF3 measure “surprise” in an analysis of Darwin's reading decisions based on the divergence between two probability distributions 
From an analysis perspective, the unit of text that we are labeling (or annotating, or coding), either automatic or manual, can sometimes be different than one's final unit of analysis  For example, if in a study on media frames in news stories, the theoretical framework and research question point toward frames at the story level (e g , what is the overall causal analysis of the news article?), the story must be the unit of analysis  Yet it is often difficult to validly and reliably code a single frame at the story level  Multiple perspectives are likely to sit side-by-side in a story  Thus, an article on income inequality might point to multiple causes, such as globalization, education, and tax policies  Coding at the sentence level would detect each of these causal explanations individually, but this information would need to be somehow aggregated to determine the overall story-level frame  Sometimes scholars solve this problem by only examining headlines and lead paragraphs, arguing that based on journalistic convention, the most important information can be found at the beginning of a story  However, this leads to a return to a shorter, less nuanced analysis 
From a computational perspective, the unit of text can also make a huge difference, especially when we are using bag-of-words models, where word order within a unit does not matter  Small segments, like tweets, sometimes do not have enough information to make their semantic context clear  In contrast, larger segments, like novels, have too much variation, making it difficult to train focused models  Finding a good segmentation sometimes means combining short documents and subdividing long documents  The word “document" can therefore be misleading  But it is so ingrained in the common NLP lexicon that we use it anyway in this article 
For insight-driven text analysis, it is often critical that high-level patterns can be communicated  Furthermore, interpretable models make it easier to find spurious features, to do error analysis, and to support interpretation of results  Some approaches are effective for prediction, but harder to interpret  The value we place on interpretability can therefore influence the approach we choose  There is an increasing interest in developing interpretable or transparent models in the NLP and machine learning communities 
Annotation
Many studies involve human coders  Sometimes the goal is to fully code the data, but in a computational analysis we often use the labels (or annotations) to train machine learning models to automatically recognize them, and to identify language patterns that are associated with these labels  For example, for a project analyzing rumors online BIBREF27 , conversation threads were annotated along different dimensions, including rumor versus non-rumor and stance towards a rumor 
The collection of annotation choices make up an annotation scheme (or “codebook”)  Existing schemes and annotations can be useful as starting points  Usually settling on an annotation scheme requires several iterations, in which the guidelines are updated and annotation examples are added  For example, a political scientist could use a mixed deductive-inductive strategy for developing a codebook  She starts by laying out a set of theory-driven deductive coding rules, which means that the broad principles of the coding rules are laid out without examining examples first  These are then tested (and possibly adjusted) based on a sample of the data  In line with Adcock and Collier's notion of “content validity” BIBREF13 , the goal is to assess whether the codebook adequately captures the systematized concept  By looking at the data themselves, she gains a better sense of whether some things have been left out of the coding rules and whether anything is superfluous, misleading, or confusing  Adjustments are made and the process is repeated, often with another researcher involved 
The final annotations can be collected using a crowdsourcing platform, a smaller number of highly-trained annotators, or a group of experts  Which type of annotator to use should be informed by the complexity and specificity of the concept  For more complex concepts, highly-trained or expert annotators tend to produce more reliable results  However, complex concepts can sometimes be broken down into micro-tasks that can be performed independently in parallel by crowdsourced annotators  Concepts from highly specialized domains may require expert annotators  In all cases, however, some training will be required, and the training phase should involve continual checks of inter-annotator agreement (i e  intercoder reliability) or checks against a gold standard (e g  quizzes in crowdsourcing platforms) 
We also need to decide how inter-annotator agreement will be measured and what an acceptable level of agreement would be  Krippendorff's alpha is frequently used in the social sciences, but the right measure depends on the type of data and task  For manual coding, we can continually check inter-annotator agreement and begin introducing checks of intra-annotator agreement, too  For most communication scholars using only manual content analysis, an acceptable rate of agreement is achieved when Krippendorf's alpha reaches 0 80 or above  When human-coded data are used to validate machine learning algorithms, the reliability of the human-coded data is even more important  Disagreement between annotators can signal weaknesses of the annotation scheme, or highlight the inherent ambiguity in what we are trying to measure  Disagreement itself can be meaningful and can be integrated in subsequent analyses BIBREF28 , BIBREF29  
Data pre-processing
Preparing the data can be a complex and time-consuming process, often involving working with partially or wholly unstructured data  The pre-processing steps have a big impact on the operationalizations, subsequent analyses and reproducibility efforts BIBREF30 , and they are usually tightly linked to what we intend to measure  Unfortunately, these steps tend to be underreported, but documenting the pre-processing choices made is essential and is analogous to recording the decisions taken during the production of a scholarly edition or protocols in biomedical research  Data may also vary enormously in quality, depending on how it has been generated  Many historians, for example, work with text produced from an analogue original using Optical Character Recognition (OCR)  Often, there will be limited information available regarding the accuracy of the OCR, and the degree of accuracy may even vary within a single corpus (e g  where digitized text has been produced over a period of years, and the software has gradually improved)  The first step, then, is to try to correct for common OCR errors  These will vary depending on the type of text, the date at which the `original' was produced, and the nature of the font and typesetting 
One step that almost everyone takes is to tokenize the original character sequence into the words and word-like units  Tokenization is a more subtle and more powerful process than people expect  It is often done using regular expressions or scripts that have been circulating within the NLP community  Tokenization heuristics, however, can be badly confused by emoticons, creative orthography (e g , U$A, sh!t), and missing whitespace  Multi-word terms are also challenging  Treating them as a single unit can dramatically alter the patterns in text  Many words that are individually ambiguous have clear, unmistakable meanings as terms, like “black hole" or “European Union"  However, deciding what constitutes a multi-word term is a difficult problem  In writing systems like Chinese, tokenization is a research problem in its own right 
Beyond tokenization, common steps include lowercasing, removing punctuation, stemming (removing suffixes), lemmatization (converting inflections to a base lemma), and normalization, which has never been clearly defined, but often includes grouping abbreviations like “U S A " and “USA", ordinals like “1st" and “first", and variant spellings like “noooooo"  The main goal of these steps is to improve the ratio of tokens (individual occurrences) to types (the distinct things in a corpus)  Each step requires making additional assumptions about which distinctions are relevant: is “apple” different from “Apple”? Is “burnt” different from “burned”? Is “cool" different from “coooool"? Sometimes these steps can actively hide useful patterns, like social meaning BIBREF32   Some of us therefore try do as little modification as possible 
From a multilingual perspective, English and Chinese have an unusually simple inflectional system, and so it is statistically reasonable to treat each inflection as a unique word type  Romance languages have considerably more inflections than English; many indigenous North American languages have still more  For these languages, unseen data is far more likely to include previously-unseen inflections, and therefore, dealing with inflections is more important  On the other hand, the resources for handling inflections vary greatly by language, with European languages dominating the attention of the computational linguistics community thus far 
We sometimes also remove words that are not relevant to our goals, for example by calculating vocabulary frequencies  We construct a “stoplist” of words that we are not interested in  If we are looking for semantic themes we might remove function words like determiners and prepositions  If we are looking for author-specific styles, we might remove all words except function words  Some words are generally meaningful but too frequent to be useful within a specific collection  We sometimes also remove very infrequent words  Their occurrences are too low for robust patterns and removing them helps reducing the vocabulary size 
The choice of processing steps can be guided by theory or knowledge about the domain as well as experimental investigation  When we have labels, predictive accuracy of a model is a way to assess the effect of the processing steps  In unsupervised settings, it is more challenging to understand the effects of different steps  Inferences drawn from unsupervised settings can be sensitive to pre-processing choices BIBREF33   Stemming has been found to provide little measurable benefits for topic modeling and can sometimes even be harmful BIBREF34   All in all, this again highlights the need to document these steps 
Finally, we can also mark up the data, e g , by identifying entities (people, places, organizations, etc ) or parts of speech  Although many NLP tools are available for such tasks, they are often challenged by linguistic variation, such as orthographic variation in historical texts BIBREF35 and social media BIBREF32   Moreover, the performance of NLP tools often drops when applying them outside the training domain, such as applying tools developed on newswire texts to texts written by younger authors BIBREF36   Problems (e g , disambiguation in named entity recognition) are sometimes resolved using considerable manual intervention  This combination of the automated and the manual, however, becomes more difficult as the scale of the data increases, and the `certainty' brought by the latter may have to be abandoned 
Dictionary-based approaches
Dictionaries are frequently used to code texts in content analyses BIBREF37   Dictionaries consist of one or more categories (i e  word lists)  Sometimes the output is simply the number of category occurrences (e g , positive sentiment), thus weighting words within a category equally  In some other cases, words are assigned continuous scores  The high transparency of dictionaries makes them sometimes more suitable than supervised machine learning models  However, dictionaries should only be used if the scores assigned to words match how the words are used in the data (see BIBREF38 for a detailed discussion on limitations)  There are many off-the-shelf dictionaries available (e g , LIWC BIBREF39 )  These are often well-validated, but applying them on a new domain may not be appropriate without additional validation  Corpus- or domain-specific dictionaries can overcome limitations of general-purpose dictionaries 
The dictionaries are often manually compiled, but increasingly they are constructed semi-automatically (e g , BIBREF40 )  When we semi-automatically create a word list, we use automation to identify an initial word list, and human insight to filter it  By automatically generating the initial words lists, words can be identified that human annotators might have difficulty intuiting  By manually filtering the lists, we use our theoretical understanding of the target concept to remove spurious features 
In the introduction study, SAGE BIBREF41 was used to obtain a list of words that distinguished the text in the treatment group (subreddits that were closed by Reddit) from text in the control group (similar subreddits that were not closed)  The researchers then returned to the hate speech definition provided by the European Court of Human Rights, and manually filtered the top SAGE words based on this definition  Not all identified words fitted the definition  The others included: the names of the subreddits themselves, names of related subreddits, community-specific jargon that was not directly related to hate speech, and terms such as IQ and welfare, which were frequently used in discourses of hate speech, but had significant other uses  The word lists provided the measurement instrument for their main result, which is that the use of hate speech throughout Reddit declined after the two treatment subreddits were closed 
Supervised models
Supervised learning is frequently used to scale up analyses  For example, BIBREF42 wanted to analyze the motivations of Movember campaign participants  By developing a classifier based on a small set of annotations, they were able to expand the analysis to over 90k participants 
The choice of supervised learning model is often guided by the task definition and the label types  For example, to identify stance towards rumors based on sequential annotations, an algorithm for learning from sequential BIBREF43 or time series data BIBREF44 could be used  The features (sometimes called variables or predictors) are used by the model to make the predictions  They may vary from content-based features such as single words, sequences of words, or information about their syntactic structure, to meta-information such as user or network information  Deciding on the features requires experimentation and expert insight and is often called feature engineering  For insight-driven analysis, we are often interested in why a prediction has been made and features that can be interpreted by humans may be preferred  Recent neural network approaches often use simple features as input (such as word embeddings or character sequences), which requires less feature engineering but make interpretation more difficult 
Supervised models are powerful, but they can latch on to spurious features of the dataset  This is particularly true for datasets that are not well-balanced, and for annotations that are noisy  In our introductory example on hate speech in Reddit BIBREF0 , the annotations are automatically derived from the forum in which each post appears, and indeed, many of the posts in the forums (subreddits) that were banned by Reddit would be perceived by many as hate speech  But even in banned subreddits, not all of the content is hate speech (e g , some of the top features were self-referential like the name of the subreddit) but a classifier would learn a high weight for these features 
Even when expert annotations are available on the level of individual posts, spurious features may remain  BIBREF45 produced expert annotations of hate speech on Twitter  They found that one of the strongest features for sexism is the name of an Australian TV show, because people like to post sexist comments about the contestants  If we are trying to make claims about what inhibits or encourages hate speech, we would not want those claims to be tied to the TV show's popularity  Such problems are inevitable when datasets are not well-balanced over time, across genres, topics, etc  Especially with social media data, we lack a clear and objective definition of `balance' at this time 
The risk of supervised models latching on to spurious features reinforces the need for interpretability  Although the development of supervised models is usually performance driven, placing more emphasis on interpretability could increase the adoption of these models in insight-driven analyses  One way would be to only use models that are already somewhat interpretable, for example models that use a small number of human-interpretable features  Rather than imposing such restrictions, there is also work on generating post-hoc explanations for individual predictions (e g , BIBREF46 ), even when the underlying model itself is very complex 
Topic modeling
Topic models (e g , LDA BIBREF47 ) are usually unsupervised and therefore less biased towards human-defined categories  They are especially suited for insight-driven analysis, because they are constrained in ways that make their output interpretable  Although there is no guarantee that a “topic” will correspond to a recognizable theme or event or discourse, they often do so in ways that other methods do not  Their easy applicability without supervision and ready interpretability make topic models good for exploration  Topic models are less successful for many performance-driven applications  Raw word features are almost always better than topics for search and document classification  LSTMs and other neural network models are better as language models  Continuous word embeddings have more expressive power to represent fine-grained semantic similarities between words 
A topic model provides a different perspective on a collection  It creates a set of probability distributions over the vocabulary of the collection, which, when combined together in different proportions, best match the content of the collection  We can sort the words in each of these distributions in descending order by probability, take some arbitrary number of most-probable words, and get a sense of what (if anything) the topic is “about”  Each of the text segments also has its own distribution over the topics, and we can sort these segments by their probability within a given topic to get a sense of how that topic is used 
One of the most common questions about topic models is how many topics to use, usually with the implicit assumption that there is a “right” number that is inherent in the collection  We prefer to think of this parameter as more like the scale of a map or the magnification of a microscope  The “right” number is determined by the needs of the user, not by the collection  If the analyst is looking for a broad overview, a relatively small number of topics may be best  If the analyst is looking for fine-grained phenomena, a larger number is better 
After fitting the model, it may be necessary to circle back to an earlier phase  Topic models find consistent patterns  When authors repeatedly use a particular theme or discourse, that repetition creates a consistent pattern  But other factors can also create similar patterns, which look as good to the algorithm  We might notice a topic that has highest probability on French stopwords, indicating that we need to do a better job of filtering by language  We might notice a topic of word fragments, such as “ing”, “tion”, “inter”, indicating that we are not handling end-of-line hyphenation correctly  We may need to add to our stoplist or change how we curate multi-word terms 
Validation
The output of our measurement procedures (in the social sciences often called the “scores”) must now be assessed in terms of their reliability and validity with regard to the (systemized) concept  Reliability aims to capture repeatability, i e  the extent to which a given tool provides consistent results 
Validity assesses the extent to which a given measurement tool measures what it is supposed to measure  In NLP and machine learning, most models are primarily evaluated by comparing the machine-generated labels against an annotated sample  This approach presumes that the human output is the “gold standard" against which performance should be tested  In contrast, when the reliability is measured based on the output of different annotators, no coder is taken as the standard and the likelihood of coders reaching agreement by chance (rather than because they are “correct") is factored into the resulting statistic  Comparing against a “gold standard” suggests that the threshold for human inter- and intra-coder reliability should be particularly high 
Accuracy, as well as other measures such as precision, recall and F-score, are sometimes presented as a measure of validity, but if we do not have a genuinely objective determination of what something is supposed measure—as is often the case in text analysis—then accuracy is perhaps a better indication of reliability than of validity  In that case, validity needs to be assessed based on other techniques like those we discuss later in this section  It is also worth asking what level of accuracy is sufficient for our analysis and to what extent there may be an upper bound, especially when the labels are native to the data or when the notion of a “gold standard” is not appropriate 
For some in the humanities, validation takes the form of close reading, not designed to confirm whether the model output is correct, but to present what BIBREF48 refers to as a form of “further discovery in two directions”  Model outputs tell us something about the texts, while a close reading of the texts alongside those outputs tells us something about the models that can be used for more effective model building  Applying this circular, iterative process to 450 18th-century novels written in three languages, Piper was able to uncover a new form of “conversional novel” that was not previously captured in “literary history's received critical categories” BIBREF48  
Along similar lines, we can subject both the machine-generated output and the human annotations to another round of content validation  That is, take a stratified random sample, selecting observations from the full range of scores, and ask: Do these make sense in light of the systematized concept? If not, what seems to be missing? Or is something extraneous being captured? This is primarily a qualitative process that requires returning to theory and interrogating the systematized concept, indicators, and scores together  This type of validation is rarely done in NLP, but it is especially important when it is difficult to assess what drives a given machine learning model  If there is a mismatch between the scores and systematized concept at this stage, the codebook may need to be adjusted, human coders retrained, more training data prepared, algorithms adjusted, or in some instances, even a new analytical method adopted 
Other types of validation are also possible, such as comparing with other approaches that aim to capture the same concept, or comparing the output with external measures (e g , public opinion polls, the occurrence of future events)  We can also go beyond only evaluating the labels (or point estimates)  BIBREF16 used human judgments to not only assess the positional estimates from a scaling method of latent political traits but also to assess uncertainty intervals  Using different types of validation can increase our confidence in the approach, especially when there is no clear notion of ground truth 
Besides focusing on rather abstract evaluation measures, we could also assess the models in task-based settings using human experts  Furthermore, for insight-driven analyses, it can be more useful to focus on improving explanatory power than making small improvements in predictive performance 
Analysis
In this phase, we use our models to explore or answer our research questions  For example, given a topic model we can look at the connection between topics and metadata elements  Tags such as “hate speech" or metadata information imply a certain way of organizing the collection  Computational models provide another organization, which may differ in ways that provide more insight into how these categories manifest themselves, or fail to do so 
Moreover, when using a supervised approach, the “errors”, i e  disagreement between the system output and human-provided labels, can point towards interesting cases for closer analysis and help us reflect on our conceptualizations  In the words of BIBREF2 , they can be “opportunities for interpretation”  Other types of “failures” can be insightful as well  Sometimes there is a “dog that didn't bark” BIBREF49 –i e , something that everyone thinks we should have found, but we did not  Or, sometimes the failures are telling us about the existence of something in the data that nobody noticed, or thought important, until then (e g , the large number of travel journals in Darwin's reading lists) 
Computational text analysis is not a replacement for but rather an addition to the approaches one can take to analyze social and cultural phenomena using textual data  By moving back and forth between large-scale computational analyses and small-scale qualitative analyses, we can combine their strengths so that we can identify large-scale and long-term trends, but also tell individual stories  For example, the Reddit study on hate speech BIBREF0 raised various follow-up questions: Can we distinguish hate speech from people talking about hate speech? Did people find new ways to express hate speech? If so, did the total amount of online hate speech decrease after all? As possible next steps, a qualitative discourse analyst might examine a smaller corpus to investigate whether commenters were indeed expressing hate speech in new ways; a specialist in interview methodologies might reach out to commenters to better understand the role of online hate speech in their lives  Computational text analysis represents a step towards better understanding social and cultural phenomena, and it is in many cases better suited towards opening questions rather than closing them 
Conclusion
Insight-driven computational analysis of text is becoming increasingly common  It not only helps us see more broadly, it helps us see subtle patterns more clearly and allows us to explore radical new questions about culture and society  In this article we have consolidated our experiences, as scholars from very different disciplines, in analyzing text as social and cultural data and described how the research process often unfolds  Each of the steps in the process is time-consuming and labor-intensive  Each presents challenges  And especially when working across disciplines, the research often involves a fair amount of discussion—even negotiation—about what means of operationalization and approaches to analysis are appropriate and feasible  And yet, with a bit of perseverance and mutual understanding, conceptually sound and meaningful work results so that we can truly make use of the exciting opportunities rich textual data offers 
Acknowledgements
This work was supported by The Alan Turing Institute under the EPSRC grant EP/N510129/1  Dong Nguyen is supported with an Alan Turing Institute Fellowship (TU/A/000006)  Maria Liakata is a Turing fellow at 40%  We would also like to thank the participants of the “Bridging disciplines in analysing text as social and cultural data” workshop held at the Turing Institute (2017) for insightful discussions  The workshop was funded by a Turing Institute seed funding award to Nguyen and Liakata 
Context 3: Introduction
Headline generation is the process of creating a headline-style sentence given an input article  The research community has been regarding the task of headline generation as a summarization task BIBREF1, ignoring the fundamental differences between headlines and summaries  While summaries aim to contain most of the important information from the articles, headlines do not necessarily need to  Instead, a good headline needs to capture people's attention and serve as an irresistible invitation for users to read through the article  For example, the headline “$2 Billion Worth of Free Media for Trump”, which gives only an intriguing hint, is considered better than the summarization style headline “Measuring Trump’s Media Dominance” , as the former gets almost three times the readers as the latter  Generating headlines with many clicks is especially important in this digital age, because many of the revenues of journalism come from online advertisements and getting more user clicks means being more competitive in the market  However, most existing websites naively generate sensational headlines using only keywords or templates  Instead, this paper aims to learn a model that generates sensational headlines based on an input article without labeled data 
To generate sensational headlines, there are two main challenges  Firstly, there is a lack of sensationalism scorer to measure how sensational a headline is  Some researchers have tried to manually label headlines as clickbait or non-clickbait BIBREF2, BIBREF3  However, these human-annotated datasets are usually small and expensive to collect  To capture a large variety of sensationalization patterns, we need a cheap and easy way to collect a large number of sensational headlines  Thus, we propose a distant supervision strategy to collect a sensationalism dataset  We regard headlines receiving lots of comments as sensational samples and the headlines generated by a summarization model as non-sensational samples  Experimental results show that by distinguishing these two types of headlines, we can partially teach the model a sense of being sensational 
Secondly, after training a sensationalism scorer on our sensationalism dataset, a natural way to generate sensational headlines is to maximize the sensationalism score using reinforcement learning (RL)  However, the following shows an example of a RL model maximizing the sensationalism score by generating a very unnatural sentence, while its sensationalism scorer gave a very high score of 0 99996: UTF8gbsn 十个可穿戴产品的设计原则这消息消息可惜说明 Ten design principles for wearable devices, this message message pity introduction  This happens because the sensationalism scorer can make mistakes and RL can generate unnatural phrases which fools our sensationalism scorer  Thus, how to effectively leverage RL with noisy rewards remains an open problem  To deal with the noisy reward, we introduce Auto-tuned Reinforcement Learning (ARL)  Our model automatically tunes the ratio between MLE and RL based on how sensational the training headline is  In this way, we effectively take advantage of RL with a noisy reward to generate headlines that are both sensational and fluent 
The major contributions of this paper are as follows: 1) To the best of our knowledge, we propose the first-ever model that tackles the sensational headline generation task with reinforcement learning techniques  2) Without human-annotated data, we propose a distant supervision strategy to train a sensationalism scorer as a reward function 3) We propose a novel loss function, Auto-tuned Reinforcement Learning, to give dynamic weights to balance between MLE and RL  Our code will be released  
Sensationalism Scorer
To evaluate the sensationalism intensity score $\alpha _{\text{sen}}$ of a headline, we collect a sensationalism dataset and then train a sensationalism scorer  For the sensationalism dataset collection, we choose headlines with many comments from popular online websites as positive samples  For the negative samples, we propose to use the generated headlines from a sentence summarization model  Intuitively, the summarization model, which is trained to preserve the semantic meaning, will lose the sensationalization ability and thus the generated negative samples will be less sensational than the original one, similar to the obfuscation of style after back-translation BIBREF4  For example, an original headline like UTF8gbsn“一趟挣10万？铁总增开申通、顺丰专列" (One trip to earn 100 thousand? China Railway opens new Shentong and Shunfeng special lines) will become UTF8gbsn“中铁总将增开京广两列快递专列" (China Railway opens two special lines for express) from the baseline model, which loses the sensational phrases of UTF8gbsn“一趟挣10万？" (One trip to earn 100 thousand?)   We then train the sensationalism scorer by classifying sensational and non-sensational headlines using a one-layer CNN with a binary cross entropy loss $L_{\text{sen}}$  Firstly, 1-D convolution is used to extract word features from the input embeddings of a headline  This is followed by a ReLU activation layer and a max-pooling layer along the time dimension  All features from different channels are concatenated together and projected to the sensationalism score by adding another fully connected layer with sigmoid activation  Binary cross entropy is used to compute the loss $L_{\text{sen}}$ 
Sensationalism Scorer ::: Training Details and Dataset
For the CNN model, we choose filter sizes of 1, 3, and 5 respectively  Adam is used to optimize $L_{sen}$ with a learning rate of 0 0001  We set the embedding size as 300 and initialize it from qiu2018revisiting trained on the Weibo corpus with word and character features  We fix the embeddings during training  For dataset collection, we utilize the headlines collected in qin2018automatic, lin2019learning from Tencent News, one of the most popular Chinese news websites, as the positive samples  We follow the same data split as the original paper  As some of the links are not available any more, we get 170,754 training samples and 4,511 validation samples  For the negative training samples collection, we randomly select generated headlines from a pointer generator BIBREF0 model trained on LCSTS dataset BIBREF5 and create a balanced training corpus which includes 351,508 training samples and 9,022 validation samples  To evaluate our trained classifier, we construct a test set by randomly sampling 100 headlines from the test split of LCSTS dataset and the labels are obtained by 11 human annotators  Annotations show that 52% headlines are labeled as positive and 48% headlines as negative by majority voting (The detail on the annotation can be found in Section SECREF26) 
Sensationalism Scorer ::: Results and Discussion
Our classifier achieves 0 65 accuracy and 0 65 averaged F1 score on the test set while a random classifier would only achieve 0 50 accuracy and 0 50 averaged F1 score  This confirms that the predicted sensationalism score can partially capture the sensationalism of headlines  On the other hand, a more natural choice is to take headlines with few comments as negative examples  Thus, we train another baseline classifier on a crawled balanced sensationalism corpus of 84k headlines where the positive headlines have at least 28 comments and the negative headlines have less than 5 comments  However, the results on the test set show that the baseline classifier gets 60% accuracy, which is worse than the proposed classifier (which achieves 65%)  The reason could be that the balanced sensationalism corpus are sampled from different distributions from the test set and it is hard for the trained model to generalize  Therefore, we choose the proposed one as our sensationalism scorer  Therefore, our next challenge is to show that how to leverage this noisy sensationalism reward to generate sensational headlines 
Sensational Headline Generation
Our sensational headline generation model takes an article as input and output a sensational headline  The model consists of a Pointer-Gen headline generator and is trained by ARL  The diagram of ARL can be found in Figure FIGREF6 
We denote the input article as $x=\lbrace x_1,x_2,x_3,\cdots ,x_M\rbrace $, and the corresponding headline as $y^*=\lbrace y_1^*,y_2^*,y_3^*,\cdots ,y_T^*\rbrace $, where $M$ is the number of tokens in an article and $T$ is the number of tokens in a headline 
Sensational Headline Generation ::: Pointer-Gen Headline Generator
We choose Pointer Generator (Pointer-Gen) BIBREF0, a widely used summarization model, as our headline generator for its ability to copy words from the input article  It takes a news article as input and generates a headline  Firstly, the tokens of each article, $\lbrace x_1,x_2,x_3,\cdots ,x_M\rbrace $, are fed into the encoder one-by-one and the encoder generates a sequence of hidden states $h_i$  For each decoding step $t$, the decoder receives the embedding for each token of a headline $y_t$ as input and updates its hidden states $s_t$  An attention mechanism following luong2015effective is used:
where $v$, $W_h$, $W_s$, and $b_{attn}$ are the trainable parameters and $h_t^*$ is the context vector  $s_t$ and $h_t^*$ are then combined to give a probability distribution over the vocabulary through two linear layers:
where $V$, $b$, $V^{^{\prime }}$, and $b^{^{\prime }}$ are trainable parameters  We use a pointer generator network to enable our model to copy rare/unknown words from the input article, giving the following final word probability:
where $x^t$ is the embedding of the input word of the decoder, $w_{h^*}^T$, $w_s^T$, $w_x^T$, and $b_{ptr}$ are trainable parameters, and $\sigma $ is the sigmoid function 
Sensational Headline Generation ::: Training Methods
We first briefly introduce MLE and RL objective functions, and a naive way to mix these two by a hyper-parameter $\lambda $  Then we point out the challenge of training with noisy reward, and propose ARL to address this issue 
Sensational Headline Generation ::: Training Methods ::: MLE and RL
A headline generation model can be trained with MLE, RL or a combination of MLE and RL  MLE training is to minimize the negative log likelihood of the training headlines  We feed $y^*$ into the decoder word by word and maximize the likelihood of $y^*$  The loss function for MLE becomes
For RL training, we choose the REINFORCE algorithm BIBREF6  In the training phase, after encoding an article, a headline $y^s = \lbrace y_1^s, y_2^s, y_3^s, \cdots , y_T^s\rbrace $ is obtained by sampling from $P(w)$ from our generator, and then a reward of sensationalism or ROUGE(RG) is calculated 
We use the baseline reward $\hat{R_t}$ to reduce the variance of the reward, similar to ranzato2015sequence  To elaborate, a linear model is deployed to estimate the baseline reward $\hat{R_t}$ based on $t$-th state $o_t$ for each timestep $t$  The parameters of the linear model are trained by minimizing the mean square loss between $R$ and $\hat{R_t}$:
where $W_r$ and $b_r$ are trainable parameters  To maximize the expected reward, our loss function for RL becomes
A naive way to mix these two objective functions using a hyper-parameter $\lambda $ has been successfully incorporated in the summarization task BIBREF7  It includes the MLE training as a language model to mitigate the readability and quality issues in RL  The mixed loss function is shown as follows:
where $*$ is the reward type  Usually $\lambda $ is large, and paulus2017deep used 0 9984 
Sensational Headline Generation ::: Training Methods ::: Auto-tuned Reinforcement Learning
Applying the naive mixed training method using sensationalism score as the reward is not obvious/trivial in our task  The main reason is that our sensationalism reward is notably more noisy and more fragile than the ROUGE-L reward or abstractive reward used in the summarization task BIBREF7, BIBREF8  A higher ROUGE-L F1 reward in summarization indicates higher overlapping ratio between generation and true summary statistically, but our sensationalism reward is a learned score which is fragile to be fooled with unnatural samples 
To effectively train the model with RL under noisy sensationalism reward, our idea is to balance RL with MLE  However, we argue that the weighted ratio between MLE and RL should be sample-dependent, instead of being fixed for all training samples as in paulus2017deep, kryscinski2018improving  The reason is that, RL and MLE have inconsistent optimization objectives  When the training headline is non-sensational, MLE training will encourage our model to imitate the training headline (thus generating non-sensational headlines), which counteracts the effects of RL training to generate sensational headlines 
The sensationalism score is, therefore, used to give dynamic weight to MLE and RL  Our ARL loss function becomes:
If $\alpha _{\text{sen}}(y^*)$ is high, meaning the training headline is sensational, our loss function encourages our model to imitate the sample more using the MLE training  If $\alpha _{\text{sen}}(y^*)$ is low, our loss function replies on RL training to improve the sensationalism  Note that the weight $\alpha _{\text{sen}}(y^*)$ is different from our sensationalism reward $\alpha _{\text{sen}}(y^s)$ and we call the loss function Auto-tuned Reinforcement Learning, because the ratio between MLE and RL are well “tuned” towards different samples 
Sensational Headline Generation ::: Dataset
We use LCSTS BIBREF5 as our dataset to train the summarization model  The dataset is collected from the Chinese microblogging website Sina Weibo  It contains over 2 million Chinese short texts with corresponding headlines given by the author of each text  The dataset is split into 2,400,591 samples for training, 10,666 samples for validation and 725 samples for testing  We tokenize each sentence with Jieba and a vocabulary size of 50000 is saved 
Sensational Headline Generation ::: Baselines and Our Models
We experiment and compare with the following models 
Pointer-Gen is the baseline model trained by optimizing $L_\text{MLE}$ in Equation DISPLAY_FORM13 
Pointer-Gen+Pos is the baseline model by training Pointer-Gen only on positive examples whose sensationalism score is larger than 0 5
Pointer-Gen+Same-FT is the model which fine-tunes Pointer-Gen on the training samples whose sensationalism score is larger than 0 1
Pointer-Gen+Pos-FT is the model which fine-tunes Pointer-Gen on the training samples whose sensationalism score is larger than 0 5
Pointer-Gen+RL-ROUGE is the baseline model trained by optimizing $L_\text{RL-ROUGE}$ in Equation DISPLAY_FORM17, with ROUGE-L BIBREF9 as the reward 
Pointer-Gen+RL-SEN is the baseline model trained by optimizing $L_\text{RL-SEN}$ in Equation DISPLAY_FORM17, with $\alpha _\text{sen}$ as the reward 
Pointer-Gen+ARL-SEN is our model trained by optimizing $L_\text{ARL-SEN}$ in Equation DISPLAY_FORM19, with $\alpha _\text{sen}$ as the reward 
Test set is the headlines from the test set 
Note that we didn't compare to Pointer-Gen+ARL-ROUGE as it is actually Pointer-GEN  Recall that $\alpha _{\text{sen}}(y^*)$ in Equation DISPLAY_FORM19 measures how good (based on reward function) is $y^*$  Then the loss function for Pointer-Gen+ARL-ROUGE will be
We also tried text style transfer baseline BIBREF10, but the generated headlines were very poor (many unknown words and irrelevant) 
Sensational Headline Generation ::: Training Details
MLE training: An Adam optimizer is used with the learning rate of 0 0001 to optimize $L_{\text{MLE}}$  The batch size is set as 128 and a one-layer, bi-directional Long Short-Term Memory (bi-LSTM) model with 512 hidden sizes and a 350 embedding size is utilized  Gradients with the l2 norm larger than 2

ground_truth: Unanswerable

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 baselines are used for evaluation?
Context 3: 0 are clipped  We stop training when the ROUGE-L f-score stops increasing 
Hybrid training: An Adam optimizer with a learning rate of 0 0001 is used to optimize $L_{\text{RL-*}}$ (Equation DISPLAY_FORM17) and $L_\text{{ARL-SEN}}$ (Equation DISPLAY_FORM19)  When training Pointer-Gen+RL-ROUGE, the best $\lambda $ is chosen based on the ROUGE-L score on the validation set  In our experiment, $\lambda $ is set as 0 95  An Adam optimizer with a learning rate of 0 001 is used to optimize $L_b$  When training Pointer-Gen+ARL-SEN, we don't use the full LCSTS dataset, but only headlines with a sensationalism score larger than 0 1 as we observe that Pointer-Gen+ARL-SEN will generate a few unnatural phrases when using full dataset  We believe the reason is the high ratio of RL during training  Figure FIGREF23 shows that the probability density near 0 is very high, meaning that in each batch, many of the samples will have a very low sensationalism score  On expectation, each sample will receive 0 239 MLE training and 0 761 RL training  This leads to RL dominanting the loss  Thus, we propose to filter samples with a minimum sensationalism score with 0 1 and it works very well  For Pointer-Gen+RL-SEN, we also set the minimum sensationalism score as 0 1, and $\lambda $ is set as 0 5 to remove unnatural phrases, making a fair comparison to Pointer-Gen+ARL-SEN 
We stop training Pointer-Gen+Same-FT, Pointer-Gen+Pos-FT, Pointer-Gen+RL-SEN and Pointer-Gen+ARL-SEN, when $\alpha _\text{sen}$ stops increasing on the validation set  Beam-search with a beam size of 5 is adopted for decoding in all models 
Sensational Headline Generation ::: Evaluation Metrics
We briefly describe the evaluation metrics below 
ROUGE: ROUGE is a commonly used evaluation metric for summarization  It measures the N-gram overlap between generated and training headlines  We use it to evaluate the relevance of generated headlines  The widely used pyrouge toolkit is used to calculate ROUGE-1 (RG-1), ROUGE-2 (RG-2), and ROUGE-L (RG-L) 
Human evaluation: We randomly sample 50 articles from the test set and send the generated headlines from all models and corresponding headlines in the test set to human annotators  We evaluate the sensationalism and fluency of the headlines by setting up two independent human annotation tasks  We ask 10 annotators to label each headline for each task  For the sensationalism annotation, each annotator is asked one question, “Is the headline sensational?”, and he/she has to choose either `yes' or `no'  The annotators were not told which system the headline is from  The process of distributing samples and recruiting annotators is managed by Crowdflower  After annotation, we define the sensationalism score as the proportion of annotations on all generated headlines from one model labeled as `yes'  For the fluency annotation, we repeat the same procedure as for the sensationalism annotation, except that we ask each annotator the question “Is the headline fluent?” We define the fluency score as the proportion of annotations on all headlines from one specific model labeled as `yes'  We put human annotation instructions in the supplemental material 
UTF8gbsn
Results
We first compare all four models, Pointer-Gen, Pointer-Gen-RL+ROUGE, Pointer-Gen-RL-SEN, and Pointer-Gen-ARL-SEN, to existing models with ROUGE in Table TABREF25 to establish that our model produces relevant headlines and we leave the sensationalism for human evaluation  Note that we only compare our models to commonly used strong summarization baselines, to validate that our implementation achieves comparable performance to existing work  In our implementation, Pointer-Gen achieves a 34 51 RG-1 score, 22 21 RG-2 score, and 31 68 RG-L score, which is similar to the results of gu2016incorporating  Pointer-Gen+ARL-SEN, although optimized for the sensationalism reward, achieves similar performance to our Pointer-Gen baseline, which means that Pointer-Gen+ARL-SEN still keeps its summarization ability  An example of headlines generated from different models in Table TABREF29 shows that Pointer-Gen and Pointer-Gen+RL-ROUGE learns to summarize the main point of the article: “The Nikon D600 camera is reported to have black spots when taking photos”  Pointer-Gen+RL-SEN makes the headline more sensational by blaming Nikon for attributing the damage to the smog  Pointer-Gen+ARL-SEN generates the most sensational headline by exaggerating the result “Getting a serious trouble!” to maximize user's attention 
We then compare different models using the sensationalism score in Table TABREF30  The Pointer-Gen baseline model achieves a 42 6% sensationalism score, which is the minimum that a typical summarization model achieves  By filtering out low-sensational headlines, Pointer-Gen+Same-FT and Pointer-Gen+Pos-FT achieves higher sensationalism scores, which implies the effectiveness of our sensationalism scorer  Our Pointer-Gen+ARL-SEN model achieves the best performance of 60 8%  This is an absolute improvement of 18 2% over the Pointer-Gen baseline  The Chi-square test on the results confirms that Pointer-Gen+ARL-SEN is statistically significantly more sensational than all the other baseline models, with the largest p-value less than 0 01  Also, we find that the test set headlines achieves 57 8% sensationalism score, much larger than Pointer-Gen baseline, which also supports our intuition that generated headlines will be less sensational than the original one  On the other hand, we found that Pointer-Gen+Pos is much worse than other baselines  The reason is that training on sensational samples alone discards around 80% of the whole training set that is also helpful for maintaining relevance and a good language model  It shows the necessity of using RL 
UTF8gbsn
In addition, both Pointer-Gen+RL-SEN and Pointer-Gen+ARL-SEN, which use the sensationalism score as the reward, obtain statistically better results than Pointer-Gen+RL-ROUGE and Pointer-Gen, with a p-value less than 0 05 by a Chi-square test  This result shows the effectiveness of RL to generate more sensational headlines  The reason is that even though our noisy classifier could also learn to classify domains, the generator during RL training is not allowed to increase the reward by shifting domains but encouraged to generate more sensational headlines, due to the consistency constraint on the domains of the headline and the article  Furthermore, Poiner-Gen+ARL-SEN gets better performance than Pointer-Gen+RL-SEN, which confirms the superiority of the ARL loss function  We also visualize in Figure FIGREF31 a comparison between Pointer-Gen+ARL-SEN and Pointer-Gen+RL-SEN according to how sensational the test set headlines are  The blue bars denote the smaller scores between the two models  For example, if the blue bar is 0 6, it means that the worse model between Pointer-Gen+RL-SEN and Pointer-Gen+ARL-SEN achieves 0 6  And the color of orange/black further indicates the better model and its score  We find that Pointer-Gen+ARL-SEN outperforms Pointer-Gen+RL-SEN for most cases  The improvement is higher when the test set headlines are not sensational (the sensationalism score is less than 0 5), which may be attributed to the higher ratio of RL training on non-sensational headlines 
Apart from the sensationalism evaluation, we measure the fluency of the headlines generated from different models  Fluency scores in Table TABREF30 show that Pointer-Gen+RL-SEN and Pointer-Gen+ARL-SEN achieve comparable fluency performance to Pointer-Gen and Pointer-Gen+RL-ROUGE  Test set headlines achieve the best performance among all models, but the difference is not statistically significant  Also, we observe that fine-tuning on sensational headlines will hurt the performance, both in sensationalism and fluency 
After manually checking the outputs, we observe that our model is able to generate sensational headlines using diverse sensationalization strategies  These strategies include, but are not limited to, creating a curiosity gap, asking questions, highlighting numbers, being emotional and emphasizing the user  Examples can be found in Table TABREF32 
Related Work
Our work is related to summarization tasks  An encoder-decoder model was first applied to two sentence-level abstractive summarization tasks on the DUC-2004 and Gigaword datasets BIBREF12  This model was later extended by selective encoding BIBREF13, a coarse to fine approach BIBREF14, minimum risk training BIBREF1, and topic-aware models BIBREF15  As long summaries were recognized as important, the CNN/Daily Mail dataset was used in nallapati2016abstractive  Graph-based attention BIBREF16, pointer-generator with coverage loss BIBREF0 are further developed to improve the generated summaries  celikyilmaz2018deep proposed deep communicating agents for representing a long document for abstractive summarization  In addition, many papers BIBREF17, BIBREF18, BIBREF19 use extractive methods to directly select sentences from articles  However, none of these work considered the sensationalism of generated outputs 
RL is also gaining popularity as it can directly optimize non-differentiable metrics BIBREF20, BIBREF21, BIBREF22  paulus2017deep proposed an intra-decoder model and combined RL and MLE to deal with summaries with bad qualities  RL has also been explored with generative adversarial networks (GANs) BIBREF23  liu2017generative applied GANs on summarization task and achieved better performance  niu2018polite tackles the problem of polite generation with politeness reward  Our work is different in that we propose a novel function to balance RL and MLE 
Our task is also related to text style transfer  Implicit methods BIBREF10, BIBREF24, BIBREF4 transfer the styles by separating sentence representations into content and style, for example using back-translationBIBREF4  However, these methods cannot guarantee the content consistency between the original sentence and transferred output BIBREF25  Explicit methods BIBREF26, BIBREF25 transfer the style by directly identifying style related keywords and modifying them  However, sensationalism is not always restricted to keywords, but the full sentence  By leveraging small human labeled English dataset, clickbait detection has been well investigated BIBREF2, BIBREF27, BIBREF3  However, these human labeled dataset are not available for other languages, such as Chinese 
Modeling sensationalism is also related to modeling emotion  Emotion has been well investigated in both word levelBIBREF28, BIBREF29 and sentence levelBIBREF30, BIBREF31, BIBREF32, BIBREF33, BIBREF34  It has also been considered an important factor in engaging interactive systemsBIBREF35, BIBREF36, BIBREF37  Although we observe that sensational headlines contain emotion, it is still not clear which emotion and how emotions will influence the sensationalism 
Conclusion and Future Work
In this paper, we propose a model that generates sensational headlines without labeled data using Reinforcement Learning  Firstly, we propose a distant supervision strategy to train the sensationalism scorer  As a result, we achieve 65% accuracy between the predicted sensationalism score and human evaluation  To effectively leverage this noisy sensationalism score as the reward for RL, we propose a novel loss function, ARL, to automatically balance RL with MLE  Human evaluation confirms the effectiveness of both our sensationalism scorer and ARL to generate more sensational headlines  Future work can be improving the sensationalism scorer and investigating the applications of dynamic balancing methods between RL and MLE in textGANBIBREF23  Our work also raises the ethical questions about generating sensational headlines, which can be further explored 
Acknowledgments
Thanks to ITS/319/16FP of Innovation Technology Commission, HKUST 16248016 of Hong Kong Research Grants Council for funding  In addition, we thank Zhaojiang Lin for helpful discussion and Yan Xu, Zihan Liu for the data collection 

ground_truth: Pointer-Gen, Pointer-Gen+Pos, Pointer-Gen+Same-FT, Pointer-Gen+Pos-FT, Pointer-Gen+RL-ROUGE, Pointer-Gen+RL-SEN