Automated Lay Language Summarization of Biomedical Scientific Reviews
- URL: http://arxiv.org/abs/2012.12573v1
- Date: Wed, 23 Dec 2020 10:01:18 GMT
- Title: Automated Lay Language Summarization of Biomedical Scientific Reviews
- Authors: Yue Guo, Wei Qiu, Yizhong Wang, Trevor Cohen
- Abstract summary: Health literacy has emerged as a crucial factor in making appropriate health decisions and ensuring treatment outcomes.
Medical jargon and the complex structure of professional language in this domain make health information especially hard to interpret.
This paper introduces the novel task of automated generation of lay language summaries of biomedical scientific reviews.
- Score: 16.01452242066412
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Health literacy has emerged as a crucial factor in making appropriate health
decisions and ensuring treatment outcomes. However, medical jargon and the
complex structure of professional language in this domain make health
information especially hard to interpret. Thus, there is an urgent unmet need
for automated methods to enhance the accessibility of the biomedical literature
to the general population. This problem can be framed as a type of translation
problem between the language of healthcare professionals, and that of the
general public. In this paper, we introduce the novel task of automated
generation of lay language summaries of biomedical scientific reviews, and
construct a dataset to support the development and evaluation of automated
methods through which to enhance the accessibility of the biomedical
literature. We conduct analyses of the various challenges in solving this task,
including not only summarization of the key points but also explanation of
background knowledge and simplification of professional language. We experiment
with state-of-the-art summarization models as well as several data augmentation
techniques, and evaluate their performance using both automated metrics and
human assessment. Results indicate that automatically generated summaries
produced using contemporary neural architectures can achieve promising quality
and readability as compared with reference summaries developed for the lay
public by experts (best ROUGE-L of 50.24 and Flesch-Kincaid readability score
of 13.30). We also discuss the limitations of the current attempt, providing
insights and directions for future work.
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