Factual Dialogue Summarization via Learning from Large Language Models

• URL: http://arxiv.org/abs/2406.14709v1
• Date: Thu, 20 Jun 2024 20:03:37 GMT
• Title: Factual Dialogue Summarization via Learning from Large Language Models
• Authors: Rongxin Zhu, Jey Han Lau, Jianzhong Qi,
• Abstract summary: Large language model (LLM)-based automatic text summarization models generate more factually consistent summaries. We employ zero-shot learning to extract symbolic knowledge from LLMs, generating factually consistent (positive) and inconsistent (negative) summaries. Our approach achieves better factual consistency while maintaining coherence, fluency, and relevance, as confirmed by various automatic evaluation metrics.
• Score: 35.63037083806503
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Factual consistency is an important quality in dialogue summarization. Large language model (LLM)-based automatic text summarization models generate more factually consistent summaries compared to those by smaller pretrained language models, but they face deployment challenges in real-world applications due to privacy or resource constraints. In this paper, we investigate the use of symbolic knowledge distillation to improve the factual consistency of smaller pretrained models for dialogue summarization. We employ zero-shot learning to extract symbolic knowledge from LLMs, generating both factually consistent (positive) and inconsistent (negative) summaries. We then apply two contrastive learning objectives on these summaries to enhance smaller summarization models. Experiments with BART, PEGASUS, and Flan-T5 indicate that our approach surpasses strong baselines that rely on complex data augmentation strategies. Our approach achieves better factual consistency while maintaining coherence, fluency, and relevance, as confirmed by various automatic evaluation metrics. We also provide access to the data and code to facilitate future research.

Related papers

• Enhancing Retrieval-Augmented LMs with a Two-stage Consistency Learning Compressor [4.35807211471107]
  This work proposes a novel two-stage consistency learning approach for retrieved information compression in retrieval-augmented language models. The proposed method is empirically validated across multiple datasets, demonstrating notable enhancements in precision and efficiency for question-answering tasks.
  arXiv  Detail & Related papers  (2024-06-04T12:43:23Z)
• Information-Theoretic Distillation for Reference-less Summarization [67.51150817011617]
  We present a novel framework to distill a powerful summarizer based on the information-theoretic objective for summarization. We start off from Pythia-2.8B as the teacher model, which is not yet capable of summarization. We arrive at a compact but powerful summarizer with only 568M parameters that performs competitively against ChatGPT.
  arXiv  Detail & Related papers  (2024-03-20T17:42:08Z)
• 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)
• mFACE: Multilingual Summarization with Factual Consistency Evaluation [79.60172087719356]
  Abstractive summarization has enjoyed renewed interest in recent years, thanks to pre-trained language models and the availability of large-scale datasets. Despite promising results, current models still suffer from generating factually inconsistent summaries. We leverage factual consistency evaluation models to improve multilingual summarization.
  arXiv  Detail & Related papers  (2022-12-20T19:52:41Z)
• Mitigating Data Sparsity for Short Text Topic Modeling by Topic-Semantic Contrastive Learning [19.7066703371736]
  We propose a novel short text topic modeling framework, Topic-Semantic Contrastive Topic Model (TSCTM) Our TSCTM outperforms state-of-the-art baselines regardless of the data augmentation availability, producing high-quality topics and topic distributions.
  arXiv  Detail & Related papers  (2022-11-23T11:33:43Z)
• Large Language Models with Controllable Working Memory [64.71038763708161]
  Large language models (LLMs) have led to a series of breakthroughs in natural language processing (NLP) What further sets these models apart is the massive amounts of world knowledge they internalize during pretraining. How the model's world knowledge interacts with the factual information presented in the context remains under explored.
  arXiv  Detail & Related papers  (2022-11-09T18:58:29Z)
• CONFIT: Toward Faithful Dialogue Summarization with Linguistically-Informed Contrastive Fine-tuning [5.389540975316299]
  Factual inconsistencies in generated summaries severely limit the practical applications of abstractive dialogue summarization. We provide a typology of factual errors with annotation data to highlight the types of errors and move away from a binary understanding of factuality. We propose a training strategy that improves the factual consistency and overall quality of summaries via a novel contrastive fine-tuning, called ConFiT.
  arXiv  Detail & Related papers  (2021-12-16T09:08:40Z)
• 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)
• InfoBERT: Improving Robustness of Language Models from An Information Theoretic Perspective [84.78604733927887]
  Large-scale language models such as BERT have achieved state-of-the-art performance across a wide range of NLP tasks. Recent studies show that such BERT-based models are vulnerable facing the threats of textual adversarial attacks. We propose InfoBERT, a novel learning framework for robust fine-tuning of pre-trained language models.
  arXiv  Detail & Related papers  (2020-10-05T20:49:26Z)

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.