Reducing Quantity Hallucinations in Abstractive Summarization (2020)

Abstract: It is well-known that abstractive summaries are subject to hallucination—including material that is not supported by the original text. While summaries can be made hallucination-free by limiting them to general phrases, such summaries would fail to be very informative. Alternatively, one can try to avoid hallucinations by verifying that any specific entities in the summary appear in the original text in a similar context. This is the approach taken by our system, Herman. The system learns to recognize and verify quantity entities (dates, numbers, sums of money, etc.) in a beam-worth of abstractive summaries produced by state-of-the-art models, in order to up-rank those summaries whose quantity terms are supported by the original text. Experimental results demonstrate that the ROUGE scores of such up-ranked summaries have a higher Precision than summaries that have not been up-ranked, without a comparable loss in Recall, resulting in higher F1. Preliminary human evaluation of up-ranked vs. original summaries shows people’s preference for the former.

## introduction automatic summarization is the task of compressing a lengthy text to a more concise version that preserves the information of the original text. common approaches are either extractive, selecting and assembling salient words, phrases and sentences from the source text to form the summary @xcite @xcite , or abstractive, generating the summary from scratch, containing novel words and phrases that are paraphrased from important parts of the original text @xcite @xcite . the latter is more challenging as it involves human-like capabilities, e.g., paraphrasing, generalizing, inferring and including article: . . . the volcano was still spewing ash on sunday, hampering rescue operations. more than a dozen people were killed when it erupted in 2014 . . . rescue teams are still scouring the area, looking for more victims who may have been killed or badly burned . . . summary: rescue teams in indonesia are searching for more than 20 people missing after the mount sinabung volcano erupted on saturday, killing at least 11 people and injuring at least 20 others. article: the scale of the criminal operation has been detailed by the three sources, who say they were . . . a victim of the fraud shown the call centre script has confirmed it matched the one read out to her when she was conned out of £5,000 . . . summary: three whistleblowers have told the bbc that they were involved in a scam that conned hundreds of talktalk customers out of more than £100,000. article: the government and the doctors' union have agreed to continue negotiating until wednesday. the talks, hosted by conciliation service acas . . . summary: talks aimed at averting the imposition of a new junior doctors' contract in england have been extended for a second day. abstractive summarization has attracted increasing attention recently, thanks to the availability of large-scale datasets @xcite @xcite and advances on neural architectures @xcite @xcite . although modern abstractive summarization systems generate relatively fluent summaries, recent work has called attention to the problem they have with factual inconsistency @xcite . that is, they produce summaries that contain hallucinated facts that are not supported by the source text. a recent study has shown that up to 30% of summaries generated by abstractive summarization systems contain hallucinated facts @xcite . such high levels of factual hallucination raise serious concern about the usefulness of abstractive summarization, especially if one believes that summaries (whether extractive or abstractive) should contain a mixture of general and specific information @xcite . this paper explores reducing the frequency of one type of hallucinated fact in abstractive summaries-hallucinated quantities. we focus on quantities not only because they are important for factual consistency, but also because, unless they are wildly inaccurate, a reader might not notice that they are hallucinated. moreover, unlike people's names (which are also frequently hallucinated), quantity entities are rarely referred to anaphorically, avoiding the need to resolve anaphoric expressions, making them an excellent testbed for the study of hallucination. the quantities we address can be broadly categorized into seven types: dates, times, percentages, monetary values, measurements, ordinals, and cardinal numbers. we present herman foot_0 , a system that learns to recognize quantities in a summary and verify their factual consistency with the source text. our system can be easily coupled with any abstractive summarization models that produce a beam-worth of candidate summaries. after verifying consistency, we use a re-ranking approach that up-rank those summaries whose quantities are supported by the source text, similar to the method proposed by @xcite . training data is automatically generated in a weakly supervised manner from a summarization dataset containing both original and synthetic data. the synthetic data is created by selecting quantity entities from the summary and replacing them with randomly selected entities from the source text that are the same type. we perform experiments on the xsum dataset @xcite which favors an abstractive modeling approach. results based on automatic evaluation using rouge @xcite demonstrate that up-ranked summaries have higher rouge precision than original summaries produced by three different summarization systems. while rouge recall of these up-ranked summaries is lower, overall rouge f 1 is higher for up-ranked summaries, showing that it is not simply a like-for-like tradeoff of recall for precision. a preliminary human evaluation study shows that subjects prefer the upranked summaries to the original summaries. ## related work recent studies have suggested that abstractive summarization systems are prone to generate summaries with hallucinated facts that cannot be supported by the source document. @xcite reported that almost 30% of the outputs of a state-ofthe-art system contain factual inconsistencies. an evaluation of summaries produced by recent stateof-the-art models via crowdsourcing suggested that 25% of the summaries have factual errors @xcite . the work also showed that rouge scores do not correlate with factual correctness, emphasizing that rouge based evaluation alone is not enough for summarization task. in addition, @xcite pointed out that current evaluation protocols correlate weakly with human judgements and do not take factual correctness into account. @xcite conducted a large scale human evaluation on the generated summaries of various abstractive summarization systems and found substantial amounts of hallucinated content in those summaries. they also concluded that summarization models initialized with pre-trained parameters perform best on not only rouge, but also human judgements of faithfulness/factuality. another line of research focused on evaluating factual consistency of summarization systems. @xcite proposed a weaklysupervised, model-based approach for evaluating factual consistency between source documents and generated summaries. they first generate training data by applying a series of transformations to randomly selected individual sentences from source documents (which they call claims) and assign them a binary label based on the type of the transformation. then they train a fact-checking model to classify the label of the claim and extract spans in both the source document and the generated summary explaining the model's decision. ## methodology let x be the article and s be the corresponding summary where both are sequences of tokens, x 1 • • • x a and s 1 • • • s n , respectively. given a (x, s) pair, our aim is to generate a tag sequence y with the same length as s (i.e., n) and a summarylevel label @xmath0, unverified}, indicating whether the summary s can be verified using x. the generated tag sequence y 1 • • • y n contains token-level labels where y @xmath1, b-u, i-u, i-v, o} indicating whether the token is verified, unverified, or other. we adopt the bio format (ramshaw and marcus, 1999) for labels since entities may span multiple tokens. to aid the recognition of quantity based entities, we also obtain a sequence of binary labels @xmath2, . . . , m n ) for the summary indicating the location of these entities. our approach consists of two steps. first, we create a synthetic, weakly-supervised dataset @xmath3 ## dataset we use the xsum dataset which was developed for abstractive document summarization @xcite . the xsum dataset consists of bbc articles, with a single-sentence summary of each. this summary is a professionally written introductory sentence, typically written by the author of the article, which is separated from the article, with the remaining text taken to be the document. this one-sentence summary, different from a headline whose purpose is to attract readers to read the article, draws on information distributed in various parts of the document and displays multiple levels of abstraction including paraphrasing, fusion, synthesis, and inference. the dataset contains 204,045 instances for training, 11,332 instances for validation, and 11,334 instances for testing. overall, 55% of the instances contain at least one quantity. the distribution of quantity entities is shown in in order to obtain the dataset used to train her-man, we follow procedures described in section 3.1. we apply same pre-processing steps noted by @xcite . we also truncate the input document to 400 tokens and limit the length of the summary to 90 tokens. the dataset size for training, validation, and test are 190,370, 10,594, and 10,592, respectively. as noted in section 3.1, the dataset we use is smaller than the xsum dataset because we discard instances which cannot be perturbed to obtain an unverified summary. ## experiments for all experiments, we set the hidden dimensions to 256, the word embeddings to 100, and the vocabulary size to 50k. the word embeddings are initialized using pre-trained glove @xcite . we also experimented using a pre-trained, base-uncased bert @xcite for word embedding initialization. our training used the adam optimizer (kingma and ba, 2015) with a learning rate of 0.001. we also use gradient clipping with a maximum gradient norm of 5 and we do not use any kind of regularization. we use loss on the validation set to perform early stopping. we set α to 0.66, suggesting local verification is more important than global verification. our model was trained on a single geforce gtx 1080 ti gpu with a batch size of 32. we use pytorch @xcite for our model implementation. for crf, we used the allennlp library @xcite with constrained decoding for the bio scheme. to evaluate our verification model, we need outputs from abstractive summarization systems. we obtain those from three selected systems: tconvs2s @xcite , bertsum @xcite , and bart @xcite using pre-trained checkpoints provided by the authors. ## conclusions in this paper, we addressed the problem of hallucinated quantities in summaries generated by abstractive summarization systems. we introduced herman, a novel approach to recognize and verify quantities in these summaries. experimental results demonstrate that up-ranked summaries have a higher rouge precision and f 1 than original summaries produced by a summarization system, indicating our approach reduces hallucinated quantities while still encourage the inclusion of quantity entities. through human evaluation, we showed that summaries up-ranked by our proposed model are felt to be more faithful than the summaries directly generated by a summarization system. we also discovered that simple re-ranking strategies, such as the selection of the shortest summary from the beam search, can yield strong performance, if one doesn't care whether a summary communicates specific quantities. we also found that our approach was limited by its use of the xsum dataset, where factual information in the summary sometimes cannot be verified using the article due to the fact that the summary is simply the first sentence of the original article. in the future, we would like to explore the option of incorporating the verification model into training and inference to improve factual correctness of generated summaries.