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Automatic Text Summarization by Non-topic Relevance Estimation

Abstract : We investigate a novel framework for Automatic Text Summarization. In this framework underlying language-use features are learned from a minimal sample corpus. We argue the low complexity of this kind of features allows relying in generalization ability of a learning machine, rather than in diverse human-abstracted summaries. In this way, our method reliably estimates a relevance measure for predicting summary candidature scores, regardless topics in unseen documents. Our output summaries are comparable to the state-of-the-art. Thus we show that in order to extract meaning summaries, it is not crucial what is being said; but rather how it is being said.
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Contributor : Catherine Beaussier <>
Submitted on : Tuesday, February 21, 2017 - 3:47:08 PM
Last modification on : Tuesday, May 26, 2020 - 3:24:35 AM

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Ignacio Arroyo-Fernandez, Juan-Manuel Torres-Moreno, Gerardo Sierra, Luis Adrian Cabrera-Diego. Automatic Text Summarization by Non-topic Relevance Estimation. KDIR: PROCEEDINGS OF THE 8TH INTERNATIONAL JOINT CONFERENCE ON KNOWLEDGE DISCOVERY, KNOWLEDGE ENGINEERING AND KNOWLEDGE MANAGEMENT - VOL. 1, 2016, Porto, Portugal. pp.89--100, ⟨10.5220/0006053400890100⟩. ⟨hal-01473135⟩



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