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NapSS : paragraph-level medical text simplification via narrative prompting and sentence-matching summarization
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Lu, Junru, Li, Jiazheng, Wallace, Byron, He, Yulan and Pergola, Gabriele (2023) NapSS : paragraph-level medical text simplification via narrative prompting and sentence-matching summarization. In: The 17th Conference of the European Chapter of the Association for Computational Linguistics, Dubrovnik, Croatia, 2-6 May 2023. Published in: Findings of the Association for Computational Linguistics: EACL 2023 pp. 1079-1091.
An open access version can be found in:
Official URL: https://aclanthology.org/2023.findings-eacl.80
Abstract
Accessing medical literature is difficult for laypeople as the content is written for specialists and contains medical jargon. Automated text simplification methods offer a potential means to address this issue. In this work, we propose a summarize-then-simplify two-stage strategy, which we call NapSS, identifying the relevant content to simplify while ensuring that the original narrative flow is preserved. In this approach, we first generate reference summaries via sentence matching between the original and the simplified abstracts. These summaries are then used to train an extractive summarizer, learning the most relevant content to be simplified. Then, to ensure the narrative consistency of the simplified text, we synthesize auxiliary narrative prompts combining key phrases derived from the syntactical analyses of the original text. Our model achieves results significantly better than the seq2seq baseline on an English medical corpus, yielding 3% 4% absolute improvements in terms of lexical similarity, and providing a further 1.1% improvement of SARI score when combined with the baseline. We also highlight shortcomings of existing evaluation methods, and introduce new metrics that take into account both lexical and high-level semantic similarity. A human evaluation conducted on a random sample of the test set further establishes the effectiveness of the proposed approach. Codes and models are released here: https://github.com/LuJunru/NapSS.
Item Type: | Conference Item (Paper) | ||||
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||
Journal or Publication Title: | Findings of the Association for Computational Linguistics: EACL 2023 | ||||
Publisher: | Association for Computational Linguistics | ||||
Place of Publication: | Dubrovnik, Croatia | ||||
Official Date: | May 2023 | ||||
Dates: |
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Page Range: | pp. 1079-1091 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Access rights to Published version: | Open Access (Creative Commons) | ||||
Conference Paper Type: | Paper | ||||
Title of Event: | The 17th Conference of the European Chapter of the Association for Computational Linguistics | ||||
Type of Event: | Conference | ||||
Location of Event: | Dubrovnik, Croatia | ||||
Date(s) of Event: | 2-6 May 2023 | ||||
Open Access Version: |
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