DYLE: Dynamic Latent Extraction for Abstractive Long-Input Summarization

• URL: http://arxiv.org/abs/2110.08168v1
• Date: Fri, 15 Oct 2021 15:53:32 GMT
• Title: DYLE: Dynamic Latent Extraction for Abstractive Long-Input Summarization
• Authors: Ziming Mao, Chen Henry Wu, Ansong Ni, Yusen Zhang, Rui Zhang, Tao Yu, Budhaditya Deb, Chenguang Zhu, Ahmed H. Awadallah, Dragomir Radev
• Abstract summary: Transformer-based models have achieved state-of-the-art performance on short text summarization. We present a new approach for long-input summarization: Dynamic Latent Extraction for Abstractive Summarization. Our model significantly outperforms the current state-of-the-art, including a 6.21 ROUGE-2 improvement on GovReport and a 2.13 ROUGE-1 improvement on QMSum.
• Score: 19.693324720440017
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Transformer-based models have achieved state-of-the-art performance on short text summarization. However, they still struggle with long-input summarization. In this paper, we present a new approach for long-input summarization: Dynamic Latent Extraction for Abstractive Summarization. We jointly train an extractor with an abstractor and treat the extracted text snippets as the latent variable. We propose extractive oracles to provide the extractor with a strong learning signal. We introduce consistency loss, which encourages the extractor to approximate the averaged dynamic weights predicted by the generator. We conduct extensive tests on two long-input summarization datasets, GovReport (document) and QMSum (dialogue). Our model significantly outperforms the current state-of-the-art, including a 6.21 ROUGE-2 improvement on GovReport and a 2.13 ROUGE-1 improvement on QMSum. Further analysis shows that the dynamic weights make our generation process highly interpretable. Our code will be publicly available upon publication.

Related papers

• Towards Enhancing Coherence in Extractive Summarization: Dataset and Experiments with LLMs [70.15262704746378]
  We propose a systematically created human-annotated dataset consisting of coherent summaries for five publicly available datasets and natural language user feedback. Preliminary experiments with Falcon-40B and Llama-2-13B show significant performance improvements (10% Rouge-L) in terms of producing coherent summaries.
  arXiv  Detail & Related papers  (2024-07-05T20:25:04Z)
• Efficient and Interpretable Compressive Text Summarisation with Unsupervised Dual-Agent Reinforcement Learning [36.93582300019002]
  We propose an efficient and interpretable compressive summarisation method using unsupervised dual-agent reinforcement learning. Our model achieves promising performance and a significant improvement on Newsroom in terms of the ROUGE metric.
  arXiv  Detail & Related papers  (2023-06-06T05:30:49Z)
• Generating Multiple-Length Summaries via Reinforcement Learning for Unsupervised Sentence Summarization [44.835811239393244]
  Sentence summarization shortens given texts while maintaining core contents of the texts. Unsupervised approaches have been studied to summarize texts without human-written summaries. We devise an abstractive model based on reinforcement learning without ground-truth summaries.
  arXiv  Detail & Related papers  (2022-12-21T08:34:28Z)
• Improving Multi-Document Summarization through Referenced Flexible Extraction with Credit-Awareness [21.037841262371355]
  A notable challenge in Multi-Document Summarization (MDS) is the extremely-long length of the input. We present an extract-then-abstract Transformer framework to overcome the problem. We propose a loss weighting mechanism that makes the model aware of the unequal importance for the sentences not in the pseudo extraction oracle.
  arXiv  Detail & Related papers  (2022-05-04T04:40:39Z)
• HETFORMER: Heterogeneous Transformer with Sparse Attention for Long-Text Extractive Summarization [57.798070356553936]
  HETFORMER is a Transformer-based pre-trained model with multi-granularity sparse attentions for extractive summarization. Experiments on both single- and multi-document summarization tasks show that HETFORMER achieves state-of-the-art performance in Rouge F1.
  arXiv  Detail & Related papers  (2021-10-12T22:42:31Z)
• StreamHover: Livestream Transcript Summarization and Annotation [54.41877742041611]
  We present StreamHover, a framework for annotating and summarizing livestream transcripts. With a total of over 500 hours of videos annotated with both extractive and abstractive summaries, our benchmark dataset is significantly larger than currently existing annotated corpora. We show that our model generalizes better and improves performance over strong baselines.
  arXiv  Detail & Related papers  (2021-09-11T02:19:37Z)
• Transductive Learning for Abstractive News Summarization [24.03781438153328]
  We propose the first application of transductive learning to summarization. We show that our approach yields state-of-the-art results on CNN/DM and NYT datasets.
  arXiv  Detail & Related papers  (2021-04-17T17:33:12Z)
• Knowledge Graph-Augmented Abstractive Summarization with Semantic-Driven Cloze Reward [42.925345819778656]
  We present ASGARD, a novel framework for Abstractive Summarization with Graph-Augmentation and semantic-driven RewarD. We propose the use of dual encoders---a sequential document encoder and a graph-structured encoder---to maintain the global context and local characteristics of entities. Results show that our models produce significantly higher ROUGE scores than a variant without knowledge graph as input on both New York Times and CNN/Daily Mail datasets.
  arXiv  Detail & Related papers  (2020-05-03T18:23:06Z)
• Few-Shot Learning for Opinion Summarization [117.70510762845338]
  Opinion summarization is the automatic creation of text reflecting subjective information expressed in multiple documents. In this work, we show that even a handful of summaries is sufficient to bootstrap generation of the summary text. Our approach substantially outperforms previous extractive and abstractive methods in automatic and human evaluation.
  arXiv  Detail & Related papers  (2020-04-30T15:37:38Z)
• Exploring Explainable Selection to Control Abstractive Summarization [51.74889133688111]
  We develop a novel framework that focuses on explainability. A novel pair-wise matrix captures the sentence interactions, centrality, and attribute scores. A sentence-deployed attention mechanism in the abstractor ensures the final summary emphasizes the desired content.
  arXiv  Detail & Related papers  (2020-04-24T14:39:34Z)
• AMR Parsing via Graph-Sequence Iterative Inference [62.85003739964878]
  We propose a new end-to-end model that treats AMR parsing as a series of dual decisions on the input sequence and the incrementally constructed graph. We show that the answers to these two questions are mutually causalities. We design a model based on iterative inference that helps achieve better answers in both perspectives, leading to greatly improved parsing accuracy.
  arXiv  Detail & Related papers  (2020-04-12T09:15:21Z)

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.