Improving Factuality of Abstractive Summarization via Contrastive Reward Learning

• URL: http://arxiv.org/abs/2307.04507v1
• Date: Mon, 10 Jul 2023 12:01:18 GMT
• Title: Improving Factuality of Abstractive Summarization via Contrastive Reward Learning
• Authors: I-Chun Chern, Zhiruo Wang, Sanjan Das, Bhavuk Sharma, Pengfei Liu and Graham Neubig
• Abstract summary: We propose a simple but effective contrastive learning framework that incorporates recent developments in reward learning and factuality metrics. Empirical studies demonstrate that the proposed framework enables summarization models to learn from feedback of factuality metrics.
• Score: 77.07192378869776
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern abstractive summarization models often generate summaries that contain hallucinated or contradictory information. In this paper, we propose a simple but effective contrastive learning framework that incorporates recent developments in reward learning and factuality metrics. Empirical studies demonstrate that the proposed framework enables summarization models to learn from feedback of factuality metrics using contrastive reward learning, leading to more factual summaries by human evaluations. This suggests that further advances in learning and evaluation algorithms can feed directly into providing more factual summaries.

Related papers

• Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback [57.816210168909286]
  We leverage recent progress on textual entailment models to address this problem for abstractive summarization systems. We use reinforcement learning with reference-free, textual entailment rewards to optimize for factual consistency. Our results, according to both automatic metrics and human evaluation, show that our method considerably improves the faithfulness, salience, and conciseness of the generated summaries.
  arXiv  Detail & Related papers  (2023-05-31T21:04:04Z)
• Analyzing and Evaluating Faithfulness in Dialogue Summarization [67.07947198421421]
  We first perform the fine-grained human analysis on the faithfulness of dialogue summaries and observe that over 35% of generated summaries are faithfully inconsistent respective the source dialogues. We present a new model-level faithfulness evaluation method. It examines generation models with multi-choice questions created by rule-based transformations.
  arXiv  Detail & Related papers  (2022-10-21T07:22:43Z)
• CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization [6.017006996402699]
  We study generating abstractive summaries that are faithful and factually consistent with the given articles. A novel contrastive learning formulation is presented, which leverages both reference summaries, as positive training data, and automatically generated erroneous summaries, as negative training data, to train summarization systems that are better at distinguishing between them.
  arXiv  Detail & Related papers  (2021-09-19T20:05:21Z)
• Dialogue Summarization with Supporting Utterance Flow Modeling and Fact Regularization [58.965859508695225]
  We propose an end-to-end neural model for dialogue summarization with two novel modules. The supporting utterance flow modeling helps to generate a coherent summary by smoothly shifting the focus from the former utterances to the later ones. The fact regularization encourages the generated summary to be factually consistent with the ground-truth summary during model training.
  arXiv  Detail & Related papers  (2021-08-03T03:09:25Z)
• Improving Factual Consistency of Abstractive Summarization via Question Answering [25.725873545789046]
  We present an approach to address factual consistency in summarization. We first propose an efficient automatic evaluation metric to measure factual consistency. We then propose a novel learning algorithm that maximizes the proposed metric during model training.
  arXiv  Detail & Related papers  (2021-05-10T19:07:21Z)
• Unsupervised Reference-Free Summary Quality Evaluation via Contrastive Learning [66.30909748400023]
  We propose to evaluate the summary qualities without reference summaries by unsupervised contrastive learning. Specifically, we design a new metric which covers both linguistic qualities and semantic informativeness based on BERT. Experiments on Newsroom and CNN/Daily Mail demonstrate that our new evaluation method outperforms other metrics even without reference summaries.
  arXiv  Detail & Related papers  (2020-10-05T05:04:14Z)
• Generating (Factual?) Narrative Summaries of RCTs: Experiments with Neural Multi-Document Summarization [22.611879349101596]
  We evaluate modern neural models for abstractive summarization of relevant article abstracts from systematic reviews. We find that modern summarization systems yield consistently fluent and relevant synopses, but that they are not always factual.
  arXiv  Detail & Related papers  (2020-08-25T22:22:50Z)
• Enhancing Factual Consistency of Abstractive Summarization [57.67609672082137]
  We propose a fact-aware summarization model FASum to extract and integrate factual relations into the summary generation process. We then design a factual corrector model FC to automatically correct factual errors from summaries generated by existing systems.
  arXiv  Detail & Related papers  (2020-03-19T07:36:10Z)
• Learning by Semantic Similarity Makes Abstractive Summarization Better [13.324006587838522]
  We compare the generated summaries from recent LM, BART, and the reference summaries from a benchmark dataset, CNN/DM. Interestingly, model-generated summaries receive higher scores relative to reference summaries.
  arXiv  Detail & Related papers  (2020-02-18T17:59:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.