Preserve Integrity in Realtime Event Summarization

Author:

Lin Chen1ORCID,Ouyang Zhichao1,Wang Xiaoli1,Li Hui1,Huang Zhenhua2

Affiliation:

1. Xiamen University, Xiamen, China

2. South China Normal University, Guangzhou, China

Abstract

Online text streams such as Twitter are the major information source for users when they are looking for ongoing events. Realtime event summarization aims to generate and update coherent and concise summaries to describe the state of a given event. Due to the enormous volume of continuously coming texts, realtime event summarization has become the de facto tool to facilitate information acquisition. However, there exists a challenging yet unexplored issue in current text summarization techniques: how to preserve the integrity, i.e., the accuracy and consistency of summaries during the update process. The issue is critical since online text stream is dynamic and conflicting information could spread during the event period. For example, conflicting numbers of death and injuries might be reported after an earthquake. Such misleading information should not appear in the earthquake summary at any timestamp. In this article, we present a novel realtime event summarization framework called IAEA (i.e., Integrity-Aware Extractive-Abstractive realtime event summarization). Our key idea is to integrate an inconsistency detection module into a unified extractive–abstractive framework. In each update, important new tweets are first extracted in an extractive module, and the extraction is refined by explicitly detecting inconsistency between new tweets and previous summaries. The extractive module is able to capture the sentence-level attention which is later used by an abstractive module to obtain the word-level attention. Finally, the word-level attention is leveraged to rephrase words. We conduct comprehensive experiments on real-world datasets. To reduce efforts required for building sufficient training data, we also provide automatic labeling steps of which the effectiveness has been empirically verified. Through experiments, we demonstrate that IAEA can generate better summaries with consistent information than state-of-the-art approaches.

Funder

Natural Science Foundation of China

Joint Innovation Research Program of Fujian Province China

Natural Science Foundation of Fujian Province China

International Cooperation Projects of Fujian Province China

National Natural Science Foundation of China

Shanghai Committee of Science and Technology

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science

Reference73 articles.

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