Most studies on abstractive summarization re-port ROUGE scores between system and ref-erence summaries. However, we have a con-cern about thetruthfulnessof generated sum-maries : whether all facts of a generated sum-mary are mentioned in the source text. Thispaper explores improving the truthfulness inheadline generation on two popular datasets. Analyzing headlines generated by the state-of-the-art encoder-decoder model, we showthat the model sometimes generates untruthfulheadlines . We conjecture that one of the rea-sons lies in untruthful supervision data usedfor training the model. In order to quantifythe truthfulness of article-headline pairs, weconsider the textual entailment of whether anarticle entails its headline. After confirmingquite a few untruthful instances in the datasets, this study hypothesizes that removing untruth-ful instances from the supervision data mayremedy the problem of the untruthful behav-iors of the model. Building a binary classifierthat predicts an entailment relation between anarticle and its headline, we filter out untruth-ful instances from the supervision data. Exper-imental results demonstrate that the headlinegeneration model trained on filtered supervi-sion data shows no clear difference in ROUGEscores but remarkable improvements in auto-matic and manual evaluations of the generatedheadlines .
Most studies on abstractive summarization report ROUGE scores between system and reference summaries. However, we have a concern about the truthfulness of generated summaries : whether all facts of a generated summary are mentioned in the source text. This paper explores improving the truthfulness in headline generation on two popular datasets. Analyzing headlines generated by the state-of-the-art encoder-decoder model, we show that the model sometimes generates untruthful headlines . We conjecture that one of the reasons lies in untruthful supervision data used for training the model. In order to quantify the truthfulness of article-headline pairs, we consider the textual entailment of whether an article entails its headline. After confirming quite a few untruthful instances in the datasets, this study hypothesizes that removing untruthful instances from the supervision data may remedy the problem of the untruthful behaviors of the model. Building a binary classifier that predicts an entailment relation between an article and its headline, we filter out untruthful instances from the supervision data. Experimental results demonstrate that the headline generation model trained on filtered supervision data shows no clear difference in ROUGE scores but remarkable improvements in automatic and manual evaluations of the generated headlines .
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re-port
report
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ref-erence
reference
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con-cern about thetruthfulnessof generated sum-maries
concern about the truthfulness of generated summaries
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sum-mary
summary
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Thispaper
This paper
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inheadline
in headline
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showthat
show that
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untruthfulheadlines
untruthful headlines
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rea-sons
reasons
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usedfor
used for
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quantifythe
quantify the
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weconsider
we consider
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anarticle
an article
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confirmingquite
confirming quite
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untruth-ful
untruthful
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mayremedy
may remedy
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behav-iors
behaviors
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classifierthat
classifier that
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anarticle
an article
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untruth-ful
untruthful
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Exper-imental
Experimental
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headlinegeneration
headline generation
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supervi-sion
supervision
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ROUGEscores
ROUGE scores
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auto-matic
automatic
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generatedheadlines
generated headlines