论文标题
端：基于熵和多样性的灾难推文摘要
EnDSUM: Entropy and Diversity based Disaster Tweet Summarization
论文作者
论文摘要
政府机构和人道主义组织利用Twitter中在Twitter中共享的大量信息来确保危机的快速响应并提供情境更新。但是，发布的大量推文使手动标识相关的推文不可能。为了解决信息过载，有必要自动生成所有推文的摘要，以突出灾难的重要方面。在本文中，我们提出了一个基于熵和多样性的摘要，被称为末端，专门用于灾难推文摘要。我们在6个数据集上进行的全面分析表明了终结的有效性，此外，强调了端的改进范围。
The huge amount of information shared in Twitter during disaster events are utilized by government agencies and humanitarian organizations to ensure quick crisis response and provide situational updates. However, the huge number of tweets posted makes manual identification of the relevant tweets impossible. To address the information overload, there is a need to automatically generate summary of all the tweets which can highlight the important aspects of the disaster. In this paper, we propose an entropy and diversity based summarizer, termed as EnDSUM, specifically for disaster tweet summarization. Our comprehensive analysis on 6 datasets indicates the effectiveness of EnDSUM and additionally, highlights the scope of improvement of EnDSUM.
