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MEAD - A Platform for Multidocument Multilingual Text Summarization
TLDR
We describe the functionality of MEAD, a comprehensive, public domain, open source, multidocument multilingual summarization environment that has been thus far downloaded by more than 500 organizations. Expand
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Using Random Walks for Question-focused Sentence Retrieval
TLDR
We consider the problem of question-focused sentence retrieval from complex news articles describing multi-event stories published over time. Expand
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Evolutionary timeline summarization: a balanced optimization framework via iterative substitution
TLDR
We present a novel framework for the web mining problem named Evolutionary Timeline Summarization (ETS). Expand
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'Helpfulness' in online communities: a measure of message quality
TLDR
Online communities displaying textual postings require measures to combat information overload. Expand
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Biased LexRank: Passage retrieval using random walks with question-based priors
TLDR
We present Biased LexRank, a method for semi-supervised passage retrieval in the context of question answering. Expand
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What's congress doing on twitter?
TLDR
We find that officials frequently use Twitter to advertise their political positions and to provide information but rarely to request political action from their constituents or to recognize the good work of others. Expand
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CST Bank: A Corpus for the Study of Cross-document Structural Relationships
TLDR
This paper presents the Cross-document Structure Theory (CST) Bank, a collection of multi-document clusters in which pairs of sentences from different documents have been annotated for cross-document structure theory relationships. Expand
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Inferring gender of movie reviewers: exploiting writing style, content and metadata
TLDR
We explore the contribution of three types of information: 1) style, 2) content, and 3) metadata (e.g. review age, social feedback). Expand
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NewsInEssence: summarizing online news topics
TLDR
A news delivery and summarization system, acting as a user's agent, gathers and recaps news items based on specifications and interests. Expand
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Learning cross-document structural relationships using boosting
TLDR
This paper describes an empirical study that uses boosting to classify CST relationships between sentence pairs extracted from topically related documents. Expand
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