Papers with neural summarization model
SummEval: Re-evaluating Summarization Evaluation (2021.tacl-1)
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Alexander R. Fabbri, Wojciech Kryściński, Bryan McCann, Caiming Xiong, Richard Socher, Dragomir Radev
| Challenge: | a lack of comprehensive studies on evaluation metrics for text summarization hinders progress . a new study aims to improve evaluation metrics that correlate with human judgments . |
| Approach: | They propose to re-evaluate automatic evaluation metrics and share a toolkit for evaluation . they hope to promote a more complete evaluation protocol for text summarization . |
| Outcome: | The proposed evaluation metrics are inconsistent with existing evaluation protocols. |
Global Optimization under Length Constraint for Neural Text Summarization (P19-1)
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| Challenge: | GOLC increases the probabilities of generating summaries that have high evaluation scores within a desired length. |
| Approach: | They propose a global optimization method under length constraint for neural text summarization models. |
| Outcome: | The proposed method generates fewer overlength summaries while maintaining the fastest processing speed. |
Ranking Sentences for Extractive Summarization with Reinforcement Learning (N18-1)
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| Challenge: | Abstractive summarization involves various text rewriting operations and has been identified as a sequence-to-sequence problem. |
| Approach: | They propose a novel algorithm which globally optimizes the ROUGE evaluation metric through a reinforcement learning objective. |
| Outcome: | The proposed algorithm outperforms state-of-the-art extractive and abstractive systems when evaluated automatically and by humans. |
Optimizing the Factual Correctness of a Summary: A Study of Summarizing Radiology Reports (2020.acl-main)
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| Challenge: | Existing abstractive summarization models do not guarantee factual correctness of summaries . |
| Approach: | They propose a framework where models evaluate factual correctness by fact-checking it against its reference using an information extraction module. |
| Outcome: | The proposed method significantly improves the factual correctness and overall quality of outputs over a competitive neural summarization system, producing radiology summaries that approach the quality of human-authored ones. |
Hierarchical Transformers for Multi-Document Summarization (P19-1)
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| Challenge: | Existing models for multidocument summarization have been developed that can process multiple documents in a hierarchical manner. |
| Approach: | They propose a neural summarization model which can process multiple input documents and distill Transformer architecture with the ability to encode documents in a hierarchical manner. |
| Outcome: | The proposed model improves on the WikiSum dataset and can process multiple documents in a hierarchical manner. |