Automatic Text Summarization by Non-topic Relevance Estimation

Ignacio Arroyo-Fernández, Juan-Manuel Torres-Moreno, Gerardo Sierra, Luis Adrián Cabrera-Diego


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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Paper Citation

in Harvard Style

Arroyo-Fernández I., Torres-Moreno J., Sierra G. and Cabrera-Diego L. (2016). Automatic Text Summarization by Non-topic Relevance Estimation . In Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016) ISBN 978-989-758-203-5, pages 89-100. DOI: 10.5220/0006053400890100

in Bibtex Style

author={Ignacio Arroyo-Fernández and Juan-Manuel Torres-Moreno and Gerardo Sierra and Luis Adrián Cabrera-Diego},
title={Automatic Text Summarization by Non-topic Relevance Estimation},
booktitle={Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016)},

in EndNote Style

JO - Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016)
TI - Automatic Text Summarization by Non-topic Relevance Estimation
SN - 978-989-758-203-5
AU - Arroyo-Fernández I.
AU - Torres-Moreno J.
AU - Sierra G.
AU - Cabrera-Diego L.
PY - 2016
SP - 89
EP - 100
DO - 10.5220/0006053400890100