Readability Controllable Biomedical Document Summarization

Zheheng Luo, Qianqian Xie, Sophia Ananiadou


Abstract
Different from general documents, it is recognised that the ease with which people can understand a biomedical text is eminently varied, owing to the highly technical nature of biomedical documents and the variance of readers’ domain knowledge. However, existing biomedical document summarization systems have paid little attention to readability control, leaving users with summaries that are incompatible with their levels of expertise.In recognition of this urgent demand, we introduce a new task of readability controllable summarization for biomedical documents, which aims to recognise users’ readability demands and generate summaries that better suit their needs: technical summaries for experts and plain language summaries (PLS) for laymen.To establish this task, we construct a corpus consisting of biomedical papers with technical summaries and PLSs written by the authors, and benchmark multiple advanced controllable abstractive and extractive summarization models based on pre-trained language models (PLMs) with prevalent controlling and generation techniques.Moreover, we propose a novel masked language model (MLM) based metric and its variant to effectively evaluate the readability discrepancy between lay and technical summaries.Experimental results from automated and human evaluations show that though current control techniques allow for a certain degree of readability adjustment during generation, the performance of existing controllable summarization methods is far from desirable in this task.
Anthology ID:
2022.findings-emnlp.343
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2022
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4667–4680
Language:
URL:
https://aclanthology.org/2022.findings-emnlp.343
DOI:
10.18653/v1/2022.findings-emnlp.343
Bibkey:
Cite (ACL):
Zheheng Luo, Qianqian Xie, and Sophia Ananiadou. 2022. Readability Controllable Biomedical Document Summarization. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 4667–4680, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Readability Controllable Biomedical Document Summarization (Luo et al., Findings 2022)
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PDF:
https://aclanthology.org/2022.findings-emnlp.343.pdf