bigbio/meqsum

MeQSum数据集是一个用于医疗问题摘要的数据集，包含1000个消费者健康问题的摘要。该数据集在ACL 2019论文《关于消费者健康问题摘要的研究》中引入，旨在通过神经抽象模型来提高问题摘要的准确性。用户提交的自然语言问题通常比需要的更长，并包含增加问题复杂性的外围信息，导致答案检索中的假阳性显著增加。该数据集的研究探索了数据增强方法，并评估了最先进的神经抽象模型在这一新任务上的表现。

原始信息汇总

数据集概述：MeQSum

基本信息

• 语言: 英语
• 许可证: 未知
• 多语言性: 单语种
• 数据集名称: MeQSum
• 主页: https://github.com/abachaa/MeQSum
• 是否公开: 是
• 任务类型: 摘要生成

数据集描述

MeQSum是一个用于医疗问题摘要生成的数据集，包含1,000个经过摘要的消费健康问题。该数据集旨在通过神经抽象模型简化问题理解，减少答案检索中的错误正例。数据集的引入是为了解决实际应用中用户提交的自然语言问题过长且包含不必要信息的问题。

引用信息

@inproceedings{ben-abacha-demner-fushman-2019-summarization, title = "On the Summarization of Consumer Health Questions", author = "Ben Abacha, Asma and Demner-Fushman, Dina", booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics", month = jul, year = "2019", address = "Florence, Italy", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/P19-1215", doi = "10.18653/v1/P19-1215", pages = "2228--2234", 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.", }