Online social media platforms, such as Twitter, are one of the most valuable
sources of information during disaster events. Therefore, humanitarian
organizations, government agencies, and volunteers rely on a summary of this
information, i.e., tweets, for effective disaster management. Although there
are several existing supervised and unsupervised approaches for automated tweet
summary approaches, these approaches either require extensive labeled
information or do not incorporate specific domain knowledge of disasters.
Additionally, the most recent approaches to disaster summarization have
proposed BERT-based models to enhance the summary quality. However, for further
improved performance, we introduce the utilization of domain-specific knowledge
without any human efforts to understand the importance (salience) of a tweet
which further aids in summary creation and improves summary quality. In this
paper, we propose a disaster-specific tweet summarization framework, IKDSumm,
which initially identifies the crucial and important information from each
tweet related to a disaster through key-phrases of that tweet. We identify
these key-phrases by utilizing the domain knowledge (using existing ontology)
of disasters without any human intervention. Further, we utilize these
key-phrases to automatically generate a summary of the tweets. Therefore, given
tweets related to a disaster, IKDSumm ensures fulfillment of the summarization
key objectives, such as information coverage, relevance, and diversity in
summary without any human intervention. We evaluate the performance of IKDSumm
with 8 state-of-the-art techniques on 12 disaster datasets. The evaluation
results show that IKDSumm outperforms existing techniques by approximately
2-79% in terms of ROUGE-N F1-score.

本文提出了一种基于域特定知识和关键短语的 tweet 自动摘要框架 IKDSumm，在 12 个灾害数据集上进行了实验，与 8 种现有技术相比，在 ROUGE-N F1 - 分数方面提高了约 2-79％。

IKDSumm: 将关键短语纳入 BERT 用于灾难推文摘要

IKDSumm: Incorporating Key-phrases into BERT for extractive Disaster  Tweet Summarization

In this work, we aim at developing an extractive summarizer in the
multi-document setting. We implement a rank based sentence selection using
continuous vector representations along with key-phrases. Furthermore, we
propose a model to tackle summary coherence for increasing readability. We
conduct experiments on the Document Understanding Conference (DUC) 2004
datasets using ROUGE toolkit. Our experiments demonstrate that the methods
bring significant improvements over the state of the art methods in terms of
informativity and coherence.

该研究旨在开发一种在多文档环境中的提取式摘要生成器，使用基于排名的句子选择方法以及连续向量表示和关键短语，并提出了一种模型来解决摘要连贯性问题，以提高可读性。实验结果表明，在信息量和连贯性方面，该方法相对于现有方法有了显著的改进。