sumedh/MeQSum

MeQSum是一个用于医疗问题摘要生成的数据集，包含1000个消费者健康问题的摘要。该数据集在ACL 2019论文《On the Summarization of Consumer Health Questions》中引入，旨在通过神经抽象模型来简化复杂的医疗问题，提高问题回答的准确性。

MeQSum is a corpus for medical question summarization, containing 1,000 summarized consumer health questions. Introduced in the ACL 2019 paper On the Summarization of Consumer Health Questions, it aims to simplify the question understanding process through neural abstractive models and reduce false positives in answer retrieval.

原始信息汇总

MeQSum 数据集

概述

MeQSum 数据集是一个用于医学问题摘要的语料库，首次在 ACL 2019 论文 "On the Summarization of Consumer Health Questions" 中介绍。该数据集包含 1,000 个经过摘要的消费者健康问题。

数据集信息

• 问题类型: 摘要
• 语言: 英语
• 多语言性: 单语
• 任务标识: 摘要

引用信息

bibtex @Inproceedings{MeQSum, author = {Asma {Ben Abacha} and Dina Demner-Fushman}, title = {On the Summarization of Consumer Health Questions}, booktitle = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, ACL 2019, Florence, Italy, July 28th - August 2}, year = {2019}, abstract = {Question understanding is one of the main challenges in question answering. In real world applications, users often submit natural language questions that are longer than needed and include peripheral information that increases the complexity of the question, leading to substantially more false positives in answer retrieval. In this paper, we study neural abstractive models for medical question summarization. We introduce the MeQSum corpus of 1,000 summarized consumer health questions. We explore data augmentation methods and evaluate state-of-the-art neural abstractive models on this new task. In particular, we show that semantic augmentation from question datasets improves the overall performance, and that pointer-generator networks outperform sequence-to-sequence attentional models on this task, with a ROUGE-1 score of 44.16%. We also present a detailed error analysis and discuss directions for improvement that are specific to question summarization. }}