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Event phase oriented news summarization

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Abstract

Event summarization is a task to generate a single, concise textual representation of an event. This task does not consider multiple development phases in an event. However, news articles related to long and complicated events often involve multiple phases. Thus, traditional approaches for event summarization generally have difficulty in capturing event phases in summarization effectively. In this paper, we define the task of Event Phase Oriented News Summarization (EPONS). In this approach, we assume that a summary contains multiple timelines, each corresponding to an event phase. We model the semantic relations of news articles via a graph model called Temporal Content Coherence Graph. A structural clustering algorithm EPCluster is designed to separate news articles into several groups corresponding to event phases. We apply a vertex-reinforced random walk to rank news articles. The ranking results are further used to create timelines. Extensive experiments conducted on multiple datasets show the effectiveness of our approach.

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Notes

  1. See background info at: https://en.wikipedia.org/wiki/Egyptian_Revolution_of_2011.

  2. One issue that needs to be discussed here is that because our dataset is relatively large and there are over k news articles in each cluster regarding an event phase, we set a uniform parameter k for all the event phases. We can also modify the definition such that k varies for different event phases without changing our algorithm.

  3. In the implementation, we set one day as a time slot and compute w t (⋅) based on publication date difference. See Figure 2a and b.

  4. Based on the definition, we can see that each news article d i and node v i has a one-to-one correspondence relationship. In the following, without ambiguity, we will use d i to represent a node and a news article interchangeably.

  5. Many other methods focus on timeline generation. However, the summaries we generates are headlines and dates, making it difficult to compare our method with them. We will investigate how to modify these algorithms for our task in the future.

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Acknowledgments

This work is partially supported by the National Key Research and Development Program of China under Grant No. 2016YFB1000904. Chengyu Wang is partially supported by the Outstanding Doctoral Dissertation Cultivation Plan of Action under Grant No. YB2016040. This manuscript is an extended version of the paper “Event Phase Extraction and Summarization” presented at WISE 2016 [44].

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Authors and Affiliations

  1. Shanghai Key Laboratory of Trustworthy Computing, School of Computer Science and Software Engineering, East China Normal University, Shanghai, 200062, China

    Chengyu Wang & Xiaofeng He

  2. School of Data Science and Engineering, East China Normal University, Shanghai, 200062, China

    Aoying Zhou

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  1. Chengyu Wang
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  2. Xiaofeng He
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Correspondence to Xiaofeng He.

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This article belongs to the Topical Collection: Special Issue on Web Information Systems Engineering

Guest Editors: Wojciech Cellary, Hua Wang, and Yanchun Zhang

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Wang, C., He, X. & Zhou, A. Event phase oriented news summarization. World Wide Web 21, 1069–1092 (2018). https://doi.org/10.1007/s11280-017-0501-x

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