Ensure the Correctness of the Summary: Incorporate Entailment Knowledge into Abstractive Sentence Summarization (C18-1)
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| Challenge: | Existing approaches focus on improving the informativeness of the summary, but ignore the correctness. |
| Approach: | They propose an entailment-aware encoder and an aML-based decoder to improve the correctness of the sentence summarization task. |
| Outcome: | The proposed model outperforms baselines on informativeness and correctness. |
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| Challenge: | Existing studies define input sentences that contain essential information in the generated summary as source sentences. |
| Approach: | They define input sentences that contain essential information in the generated summary as source sentences and analyze the source sentences to determine how abstractive summaries are made. |
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Soft Layer-Specific Multi-Task Summarization with Entailment and Question Generation (P18-1)
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| Challenge: | Recent advances on abstractive summarization have allowed substantial improvements in the quality of the model, but there is still scope for improvement. |
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Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback (2023.acl-long)
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Paul Roit, Johan Ferret, Lior Shani, Roee Aharoni, Geoffrey Cideron, Robert Dadashi, Matthieu Geist, Sertan Girgin, Leonard Hussenot, Orgad Keller, Nikola Momchev, Sabela Ramos Garea, Piotr Stanczyk, Nino Vieillard, Olivier Bachem, Gal Elidan, Avinatan Hassidim, Olivier Pietquin, Idan Szpektor
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Ranking Generated Summaries by Correctness: An Interesting but Challenging Application for Natural Language Inference (P19-1)
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| Challenge: | Recent advances on abstractive summarization have led to fluent summaries, but factual errors in generated summary still severely limit their use in practice. |
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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. |
Enhancing Factual Consistency of Abstractive Summarization (2021.naacl-main)
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| Challenge: | Abstractive summarization models often distort or fabricate facts in articles . factual inconsistency is a common problem with abstractive summaries . |
| Approach: | They propose a fact-aware summarization model FASum to extract factual relations into the summary generation process via graph attention. |
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On the Abstractiveness of Neural Document Summarization (D18-1)
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| Challenge: | Recent studies show that document summarization systems are abstractive . authors suggest that automated summarizing systems could be improved . |
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A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents (N18-2)
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Arman Cohan, Franck Dernoncourt, Doo Soon Kim, Trung Bui, Seokhwan Kim, Walter Chang, Nazli Goharian
| Challenge: | Existing abstractive summarization models focus on summarizing sentences and short documents. |
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Guided Neural Language Generation for Abstractive Summarization using Abstract Meaning Representation (D18-1)
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| Challenge: | Recent work on abstractive summarization has made progress with neural encoder-decoder architectures, but these models lack explicit semantic modeling of the source document and its summary. |
| Approach: | They extend previous work on abstractive summarization using Abstract Meaning Representation (AMR) with a neural language generation stage which they guide using the source document. |
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On Faithfulness and Factuality in Abstractive Summarization (2020.acl-main)
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| Challenge: | Existing conditional text generation models produce unfaithful and unfaithed summaries . current models accomplish a high level of fluency and coherence . |
| Approach: | They propose to use pretrained models for document summarization to better understand hallucinations . they find that textual entailment measures better correlate with faithfulness . |
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