Joint Graphical Models for Date Selection in Timeline Summarization
11855Authors
nan
Abstract
Automatic timeline summarization (TLS) generates precise, dated overviews over (often prolonged) events, such as wars or economic crises. One subtask of TLS se- lects the most important dates for an event within a certain time frame. Date selec- tion has up to now been handled via su- pervised machine learning approaches that estimate the importance of each date sepa- rately, using features such as the frequency of date mentions in news corpora. This ap- proach neglects interactions between dif- ferent dates that occur due to connections between subevents. We therefore suggest a joint graphical model for date selection. Even unsupervised versions of this model perform as well as supervised state-of-the- art approaches. With parameter tuning on training data, it outperforms prior super- vised models by a considerable margin. 1
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Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, pages 1598–1607, Beijing, China, July 26-31, 2015. c⃝2015 Association for Computational Linguistics Joint Graphical Models for Date Selection in Timeline Summarization Giang Tran L3S Research Center Leibniz-University Hannover gtran@l3s.de Eelco Herder L3S Research Center Leibniz-University Hannover herder@l3s.de Katja Markert L3S Research Center Leibniz-University Hannover and School of Computing University of Leeds markert@l3s.de Abstract Automatic timeline summarization (TLS) generates precise, dated overviews over (often prolonged) events, such as wars or economic crises. One subtask of TLS se- lects the most important dates for an event within a certain time frame. Date selec- tion has up to now been handled via su- pervised machine learning approaches that estimate the importance of each date sepa- rately, using features such as the frequency of date mentions in news corpora. This ap- proach neglects interactions between dif- ferent dates that occur due to connections between subevents. We therefore suggest a joint graphical model for date selection. Even unsupervised versions of this model perform as well as supervised state-of-the- art approaches. With parameter tuning on training data, it outperforms prior super- vised models by a considerable margin. 1 Introduction Major events (such as the Egypt revolution starting in 2011) often last over a long period of time and have impact for a considerable time afterwards. In order to find out what happened when during such an event, time-related queries to search engines are often insufficient as traditional IR does not handle time-related queries well (Foley and Allan, 2015). To provide readers with comprehensive overviews of long events, many news outlets employ time- line summaries: a timeline summary is a list of selected dates with a few sentences describing the most important events on each date. An example can be seen in Table 1. Timelines allow the reader to gain a quick overview over a complex event and to answer questions such as: How and when did the event start? What were the main consequences of the initial events? What happened to the main protagonists in the event? In addition, timelines are frequent means in education (such as history teaching) so that their generation is relevant for education as well as journalism. (a1) 2011-01-25 Egyptians hold nationwide demonstrations against the au- thoritarian rule of Hosni Mubarak, who has led the country for nearly three decades. (a2) 2011-01-26 A large security force moves into Cairo’s Tahrir Square (a3) 2011-01-28 Protesters burn down the ruling party’s headquarters, and the military is deployed. (a4) 2011-02-11 Mubarak steps down and turns power over to the military. (a5) 2011-03-19 In the first post Mubarak vote, Egyptians cast ballots on constitutional amendments . . . , including scheduling the first parliamentary and presidential elections (a8) 2012-04-20 The presidential campaign officially begins. (a10) 2012-06-24 Election officials declare Morsi the winner (a26) 2013-07-03 Egypt’s military chief says Morsi has been replaced by Adly Mansour, the chief justice of constitutional court. Table 1: A …
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Labeled Text
Automatic timeline summarization (TLS) generates precise, dated overviews over (often prolonged) events, such as wars or economic crises. One subtask of TLS selects the most important dates for an event within a certain time frame. Date selection has up to now been handled via supervised machine learning approaches that estimate the importance of each date separately, using features such as the frequency of date mentions in news corpora. This approach neglects interactions between different dates that occur due to connections between subevents. We therefore suggest a joint graphical model for date selection. Even unsupervised versions of this model perform as well as supervised state-of-theart approaches. With parameter tuning on training data, it outperforms prior supervised models by a considerable margin. 1
Publication Details
- Paper ID:
- 11855
- Year:
- 2015
- Publication:
- TIPSTER
- Publication Type:
- conf
- DOI:
- nan