Automatic Text Summarization by Non-topic Relevance Estimation

Abstract

We investigate a novel framework for Automatic Text Summarization. In this framework underlying languageuse 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.

Unsupervised sentence representations as word information series: Revisiting TF–IDF

“…The main advantage of such an approach is that there exists the possibility of studying the statistical behavior of sentence meaning. As an additional and important benefit, sentence embeddings make it possible to leverage a number of NLP tasks, such as sentence clustering, text summarization (Zhang et al, 2012;Arroyo-Fernández, 2015;Arroyo-Fernández et al, 2016;Yu et al, 2017), sentence classification (Kalchbrenner et al, 2014;Chen et al, 2017;Er et al, 2016), paraphrase identification (Yin and Schütze, 2015), semantic similarity/ relatedness and sentiment classification (Arroyo-Fernández and Meza Ruiz, 2017;Chen et al, 2017;De Boom et al, 2016;Kalchbrenner et al, 2014;Onan et al, 2017;Yazdani and Popescu-Belis, 2013).…”