Extractive is not Faithful: An Investigation of Broad Unfaithfulness Problems in Extractive Summarization (2023)

Abstract: The problems of unfaithful summaries have been widely discussed under the context of abstractive summarization. Though extractive summarization is less prone to the common unfaithfulness issues of abstractive summaries, does that mean extractive is equal to faithful? Turns out that the answer is no. In this work, we define a typology with five types of broad unfaithfulness problems (including and beyond not-entailment) that can appear in extractive summaries, including incorrect coreference, incomplete coreference, incorrect discourse, incomplete discourse, as well as other misleading information. We ask humans to label these problems out of 1600 English summaries produced by 16 diverse extractive systems. We find that 30% of the summaries have at least one of the five issues. To automatically detect these problems, we find that 5 existing faithfulness evaluation metrics for summarization have poor correlations with human judgment. To remedy this, we propose a new metric, ExtEval, that is designed for detecting unfaithful extractive summaries and is shown to have the best performance. We hope our work can increase the awareness of unfaithfulness problems in extractive summarization and help future work to evaluate and resolve these issues.

## introduction text summarization is the process of distilling the most important information from a source to produce an abridged version for a particular user or task @xcite . although there are many types of text summarization tasks, in this work, we focus on the task of general purpose single document summarization. to produce summaries, usually either extractive summarization methods, i.e., extracting sentences from the source, or abstractive summarization methods, i.e., generating novel text, are applied @xcite . abstractive summarization attracts more attention from recent works because it can produce more coherent summaries and behaves more like humans @xcite . impressive progress has been made for abstractive summarization by large-scale pre-trained models @xcite . however, unfaithfulness problems, i.e., hallucinating new information or generating content that contradicts the source, are widely spread across models and tasks @xcite . although these problems do not necessarily get captured by typically-used evaluation metrics, e.g., rouge @xcite , even minor unfaithfulness can be catastrophic and drive users away from real-world applications. therefore, an increasing volume of research has focused on analyzing @xcite @xcite , evaluating @xcite @xcite @xcite , or addressing @xcite @xcite @xcite @xcite unfaithfulness problems in abstractive summarization. extractive summarization is known to be faster, more interpretable, and more reliable @xcite @xcite . and the selection of important information is the first skill that humans learn for summarization @xcite . recently, some works discuss the trade-off between abstractiveness and faithfulness @xcite . they find that the more extractive the summary is, the more faithful it is. 2 this may give the community the impression that if the content is extracted from the source, it is guaranteed to be faithful. however, is this always true? in this work, we will show that, unfortunately, it is not. the problems of extractive summarization are usually referred as coherence, out-of-context, or readability issues @xcite @xcite . though they may sound irrelevant to faithfulness, some early works give hints of their unfaithful ingredients. @xcite describe the 'dangling' anaphora problem -sentences often contain pronouns that lose their referents when extracted out of context, and stitching together extracts may lead to a misleading interpretation of anaphors. @xcite comment on extractive methods for multi-document summarization, that extracting some similar sentences could produce a summary biases towards some sources. @xcite says that sentence extraction produces extremely incoherent text that did not seem to convey the gist of the overall controversiality of the source. these all suggest that even though all information is extracted directly from the source, the summary is not necessarily faithful to the source. however, none of these works has proposed an error typology nor quantitatively answered how unfaithful the model extracted summaries are, which motivates us to fill in this missing piece. in this work, we conduct a thorough investigation of the broad unfaithfulness problems in extractive summarization. although the literature of abstractive summarization usually limits unfaithful summaries to those that are not entailed by the source @xcite , we discuss broader unfaithfulness issues including and beyond not-entailment. we first design a typology consisting five types of unfaithfulness problems that could happen in extractive summaries: incorrect coreference, incomplete coreference, incorrect discourse, incomplete discourse, and other misleading information (see definitions in figure 2 ). among them, incorrect coreference and incorrect discourse are not-entailment based errors. an example of incorrect coreference is shown in summary 1 of figure 1 , where that in the second sentence should refer to the second document sentence -but they do leave their trash, but it incorrectly refers to the first sentence in the summary. summaries with incomplete coreferences or discourses are usually entailed by the source, but they can still lead to unfaithful interpretations. lastly, inspired by misinformation (o'connor and weatherall, 2019), our misleading information error type refers to other cases where, despite being entailed by the source, the summary still misleads the audience by selecting biased information, giving the readers wrong impressions, etc (see section 2). we ask humans to label these problems out of 1600 model extracted summaries that are produced by 16 extractive summarization systems for 100 cnn/dm english articles @xcite . these 16 systems cover both supervised and unsupervised methods, include both recent neuralbased and early graph-based models, and extract sentences or elementary discourse units (see section 3). by analyzing human annotations, we find that 30.3% of the 1600 summaries have at least one of the five types of errors. out of which, 3.9% and 15.4% summaries contain incorrect and incomplete coreferences respectively, 1.1% and 10.7% summaries have incorrect and incomplete discourses respectively, and other 4.9% summaries still mislead the audience without having coreference or discourse issues. the non-negligible error rate demonstrates that extractive is not necessarily faithful. among the 16 systems, we find that the two oracle extractive systems (that maximize rouge @xcite against the gold summary by using extracted discourse units or sentences) surprisingly have the most number of problems, while the lead3 model (the first three sentences of the source document) causes the least number of issues. we examine whether these problems can be automatically detected by 5 widely-used metrics, including rouge @xcite and 4 faithfulness evaluation metrics for abstractive summarization (factcc @xcite , dae @xcite , questeval @xcite , bertscore @xcite ). we find that, except bertscore, they have either no or small correlations with human labels. we design a new metric, exteval, for extractive summarization. it contains four sub-metrics that are used to detect incorrect coreference, incomplete coreference, incorrect or incomplete discourse, and sentiment bias, respectively. we show that exteval performs best at detecting unfaithful extractive summaries (see section 4 for more details). finally, we discuss the generalizability and future directions of 2154 document: (cnn) most climbers who try don't succeed in summiting the 29,035-foot-high mount everest, the world's tallest peak. but they do leave their trash. thousands of pounds of it. that's why an experienced climbing group from the indian army plans to trek up the 8,850-meter mountain to pick up at least 4,000 kilograms (more than 8,000 pounds) of waste from the high-altitude camps, according to india today. the mountain is part of the himalaya mountain range on the border between nepal and the tibet region. the 34-member team plans to depart for kathmandu on saturday and start the ascent in mid-may. the upcoming trip marks the 50th anniversary of the first indian team to scale mount everest [...] more than 200 climbers have died attempting to climb the peak, part of a unesco world heritage site. the indian expedition isn't @xcite testing set showing the first four types of unfaithfulness problems defined in section 2. the three summaries are generated by neusumm @xcite oracle (disco) @xcite , and bert+lstm+pn+rl @xcite , respectively. all extracted sentences or discouse units are underlined in the document. the problematic parts are bolded in the summary. the incorrect reference in the summary is marked with red, and the correct reference is marked with blue in the document. we replace non-relevant sentences with [...]. our work in section 5. in summary, our contributions are (1) a taxonomy of broad unfaithfulness problems in extractive summarization, (2) a human-labeled evaluation set with 1600 examples from 16 diverse extractive systems, (3) meta-evaluations of 5 existing metrics, (4) a new faithfulness metric (exteval) for extractive summarization. overall, we want to remind the community that even when the content is extracted from the source, there is still a chance to be unfaithful. hence, we should be aware of these problems, be able to detect them, and eventually resolve them to achieve a more reliable summarization. ## broad unfaithfulness problems in this section, we will describe the five types of broad unfaithfulness problems (figure 2 ) we identified for extractive summarization under our typology. in previous works about abstractive summarization, unfaithfulness usually only refers to the summary being not entailed by the source @xcite . the formal definition of entailment is t entails h if, typically, a human reading t would infer that h is most likely true @xcite . while we also consider being not entailed as one of the unfaithfulness prob-lems, we will show that there is still a chance to be unfaithful despite being entailed by the source. hence, we call the five error types we define here the 'broad' unfaithfulness problems, and we provide a rationale for each error type in figure 2 . the most frequent unfaithfulness problem of abstractive summarization is the presence of incorrect entities or predicates @xcite , which can never happen within extracted sentences (or elementary discourse units foot_1 ). for extractive summarization, the problems can only happen 'across' sentences (or units). 4 hence, we first define four error types about coreference and discourse. following semeval-2010 @xcite , we define coreference as the mention of the same textual references to an object in the discourse model, and we focus primarily on anaphors that require finding the correct antecedent. we ground our discourse analysis for systems that ex- ## human evaluation in this section, we describe how we ask humans to find and annotate the unfaithfulness problems. ## automatic evaluation here, we analyze whether existing automatic faithfulness evaluation metrics can detect unfaithful ex-tractive summaries. we additionally propose a new evaluation approach, exteval. ## generalizability & future work one future direction for resolving these unfaithfulness problems is to use the errors automatically detected by exteval as hints for humans or programs to fix the summary by doing necessary yet minimal edits. here we illustrate the possibility for incorrect coreference. we manually examined the automatically detected incorrect coreferences by exteval. 28 out of 32 detected incorrect coreferences are true incorrect coreferences foot_14 , which we attempt to fix by developing a simple post-edit program, similar to the revision system proposed by @xcite . the program replaces the problematic mention in the summary with the first mention in the correct coreference cluster of the document. we manually checked the corrected examples and found that 16 out of 28 were fixed correctly (see an example in figure 7 ). we leave the improvement and the extension of post-edit systems for future work. it is worth noting that all of the five error types we define in section 2 can also happen in abstractive summarization, though they are less studied and measured in the literature. to our best knowledge, frank @xcite and snac @xcite have discussed the coreference and discourse errors in the abstractive summaries. @xcite define a sentiment error as an adjective or adverb appearing in the summary that contradicts the source, while our misleading information has a more general definition. we hope that our taxonomy can shed some light for future works to explore the broad unfaithfulness of all summarization methods. ## conclusion we conducted a systematic analysis of broad unfaithfulness problems in extractive summarization. we proposed 5 error types and produced a humanlabeled evaluation set of 1600 examples. we found that (i) 30.3% of the summaries have at least one of the 5 issues, (ii) existing metrics correlate poorly with human judgment, and (iii) our new faithfulness evaluation metric exteval performs the best at identifying these problems. through this work, we want to raise the awareness of unfaithfulness issues in extractive summarization and stress that extractive is not equal to faithful. model. we also thank ido dagan for the helpful discussions. this work was supported by nsf-career award 1846185, nsf-ai engage institute drl-2112635, and a bloomberg data science ph.d. fellowship.