Authors:
Ignacio Arroyo-Fernández
1
;
Juan-Manuel Torres-Moreno
2
;
Gerardo Sierra
3
and
Luis Adrián Cabrera-Diego
2
Affiliations:
1
UNAM and UAPV, Mexico
;
2
UAPV, France
;
3
UNAM, Mexico
Keyword(s):
Automatic Text Summarization, Machine Learning, Generalization Ability, Regression Estimation.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Analytics
;
Computational Intelligence
;
Concept Mining
;
Context Discovery
;
Data Analytics
;
Data Engineering
;
Data Reduction and Quality Assessment
;
Evolutionary Computing
;
Information Extraction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Mining Text and Semi-Structured Data
;
Soft Computing
;
Symbolic Systems
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.