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        <title>Identifying the Most Dominant Event in a News Article by Mining Event Coreference Relations</title>
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        <namePart type="given">Prafulla</namePart>
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    <name type="personal">
        <namePart type="given">Ruihong</namePart>
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    <abstract>Identifying the most dominant and central event of a document, which governs and connects other foreground and background events in the document, is useful for many applications, such as text summarization, storyline generation and text segmentation. We observed that the central event of a document usually has many coreferential event mentions that are scattered throughout the document for enabling a smooth transition of subtopics. Our empirical experiments, using gold event coreference relations, have shown that the central event of a document can be well identified by mining properties of event coreference chains. But the performance drops when switching to system predicted event coreference relations. In addition, we found that the central event can be more accurately identified by further considering the number of sub-events as well as the realis status of an event.</abstract>
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    <identifier type="doi">10.18653/v1/N18-2055</identifier>
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%0 Conference Proceedings
%T Identifying the Most Dominant Event in a News Article by Mining Event Coreference Relations
%A Choubey, Prafulla Kumar
%A Raju, Kaushik
%A Huang, Ruihong
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F choubey-etal-2018-identifying
%X Identifying the most dominant and central event of a document, which governs and connects other foreground and background events in the document, is useful for many applications, such as text summarization, storyline generation and text segmentation. We observed that the central event of a document usually has many coreferential event mentions that are scattered throughout the document for enabling a smooth transition of subtopics. Our empirical experiments, using gold event coreference relations, have shown that the central event of a document can be well identified by mining properties of event coreference chains. But the performance drops when switching to system predicted event coreference relations. In addition, we found that the central event can be more accurately identified by further considering the number of sub-events as well as the realis status of an event.
%R 10.18653/v1/N18-2055
%U https://aclanthology.org/N18-2055
%U https://doi.org/10.18653/v1/N18-2055
%P 340-345

Markdown (Informal)

[Identifying the Most Dominant Event in a News Article by Mining Event Coreference Relations](https://aclanthology.org/N18-2055) (Choubey et al., NAACL 2018)

• Identifying the Most Dominant Event in a News Article by Mining Event Coreference Relations (Choubey et al., NAACL 2018)

ACL

• Prafulla Kumar Choubey, Kaushik Raju, and Ruihong Huang. 2018. Identifying the Most Dominant Event in a News Article by Mining Event Coreference Relations. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 340–345, New Orleans, Louisiana. Association for Computational Linguistics.