Summarizing a Document Stream

Takamura, Hiroya; Yokono, Hikaru; Okumura, Manabu

doi:10.1007/978-3-642-20161-5_18

Cited by 50 publications

(25 citation statements)

References 6 publications

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“…Most work does not address this issue at all. An exception is Takamura et al (2011), who ignore word matches when the matched word only appears in a summary where the time difference exceeds a pre-specified constant. However, it is left open how to set this constant and different datings of the same event below the threshold difference would again not receive any penalty.…”

Section: Rougementioning

confidence: 99%

Improving ROUGE for Timeline Summarization

Martschat¹,

Markert²

2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 2

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Section: Rougementioning

confidence: 99%

Improving ROUGE for Timeline Summarization

Martschat¹,

Markert²

2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 2

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“…Some works tried to apply conventional extractive summarization models directly, e.g., LexRank (Erkan and Radev, 2004), MEAD (Radev et al, 2004), TF-IDF (Inouye and Kalita, 2011), Integer Linear Programming (Liu et al, 2011;Takamura et al, 2011), etc. Sharif et al (2010 modeled the problem as optimal path finding on a phrase reinforcement graph.…”

Section: Related Workmentioning

confidence: 99%

Using Content-level Structures for Summarizing Microblog Repost Trees

Gao²,

Wei³

et al. 2015

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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A microblog repost tree provides strong clues on how an event described therein develops. To help social media users capture the main clues of events on microblogging sites, we propose a novel repost tree summarization framework by effectively differentiating two kinds of messages on repost trees called leaders and followers, which are derived from contentlevel structure information, i.e., contents of messages and the reposting relations. To this end, Conditional Random Fields (CRF) model is used to detect leaders across repost tree paths. We then present a variant of random-walk-based summarization model to rank and select salient messages based on the result of leader detection. To reduce the error propagation cascaded from leader detection, we improve the framework by enhancing the random walk with adjustment steps for sampling from leader probabilities given all the reposting messages. For evaluation, we construct two annotated corpora, one for leader detection, and the other for repost tree summarization. Experimental results confirm the effectiveness of our method.

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“…The emergence of Twitter motivates recent research works on mining microblogs, including microblog search [8], identifying emerging topics on Twitter [23], and summarizing tweets in a certain period [32]. A few research works have been devoted to event detection [28,29], but they focus on the detection of novel events without a global view.…”

Section: Microblog Miningmentioning

confidence: 99%

“…In [39] representative sentences are chosen based on relevance, coverage, coherence and cross-date diversity. In [32] summarization consists of median tweets in each time segment. These timeline generation methods can hardly be applied to our storyline generation problem because the asynchronism of information propagation in the microblogosphere makes it difficult to partition the timeline of an event into different phases.…”

Section: Timeline and Storyline Constructionmentioning

confidence: 99%

Generating event storylines from microblogs

Lin

et al. 2012

Proceedings of the 21st ACM International Conference on Information and Knowledge Management

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Microblogging service has emerged to be a dominant web medium for billions of individuals sharing and spreading instant news and information, therefore monitoring the event evolution on microblog sphere is crucial for providing both better user experience and deeper understanding on realtime events. In this paper we explore the problem of generating storylines from microblogs for user input queries. This problem is challenging due to the sparse, dynamic and social nature of microblogs. Given a query of an ongoing event, we propose to sketch the real-time storyline of the event by a two-level solution. We first propose a language model with dynamic pseudo relevance feedback to obtain relevant tweets, and then generate storylines via graph optimization. Comprehensive experiments on Twitter data sets demonstrate the effectiveness of the proposed methods in each level and the overall framework.

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Summarizing a Document Stream

Cited by 50 publications

References 6 publications

Improving ROUGE for Timeline Summarization

Improving ROUGE for Timeline Summarization

Using Content-level Structures for Summarizing Microblog Repost Trees

Generating event storylines from microblogs

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