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Book ChapterDOI

A Graphical Model for Football Story Snippet Synthesis from Large Scale Commentary

TLDR
This paper proposes a graphical method to synthesise story snippet from football match commentaries and shows that this model effectively extracts important information from lengthy text documents.
Abstract
Sports Commentaries offer sparse and redundant information in a lengthy format. Patterns can be observed in news articles written by sports journalists. In this paper, we propose a graphical method to synthesise story snippet from football match commentaries. Our model effectively extracts important information from lengthy text documents. Experimental study reveals that our model closely matches with human expectations. Both qualitative and quantitative analysis proves the effectiveness of our proposed method.

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References
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Proceedings ArticleDOI

Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling

TL;DR: By using simulated annealing in place of Viterbi decoding in sequence models such as HMMs, CMMs, and CRFs, it is possible to incorporate non-local structure while preserving tractable inference.
Journal ArticleDOI

LexRank: graph-based lexical centrality as salience in text summarization

TL;DR: LexRank as discussed by the authors is a stochastic graph-based method for computing relative importance of textual units for Natural Language Processing (NLP), which is based on the concept of eigenvector centrality.
Journal ArticleDOI

A Survey of Text Summarization Extractive Techniques

TL;DR: A Survey of Text Summarization Extractive techniques has been presented and it is shown that extracting important sentences, paragraphs etc. from the source text and concatenating them into shorter form conveys the most important information from the original text document.
Proceedings ArticleDOI

Summarizing sporting events using twitter

TL;DR: An algorithm that generates a journalistic summary of an event using only status updates from Twitter as a source is described, and the results are superior to the previous algorithm and approach the readability and grammaticality of the human-generated summaries.
Proceedings ArticleDOI

Micropinion generation: an unsupervised approach to generating ultra-concise summaries of opinions

TL;DR: Evaluation results show that the new unsupervised approach to generating ultra-concise summaries of opinions outperforms other state of the art summarization methods and the generated summaries are informative and readable.
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