Abstract
In this work multi-document, extractive summaries have been obtained using supervised learning algorithms in a well-known dataset (DUC 2002); the methodology has three steps: the pre-processing step, which filters irrelevant words and reduces vocabulary using stemming; the representation step, which transforms sentences into vectors; and the classification step which selects sentences for the summary. Noting that the last step is crucial because it determines the relevance of each sentence according to the information included in the embeddings. We found that the classifiers performance is not related to the summary quality mainly classifier’s goal is not aligned to summarizer’s goal, as classifier is based on selecting whole sentences, while summarization is evaluated by n-grams, for example ROUGE-n, and therefore it is relevant while comparing performances between different works in the state of the art.
This work has been possible thanks to the support of the Mexican government through the FOINS program of Consejo Nacional de Ciencia y Tecnología (CONACYT) under grants Problemas Nacionales, 5241, Cátedras CONACYT 556; the SIP-IPN research grants SIP 2083, SIP 20200640, and SIP 20200811; IPN-COFAA and IPN-EDI.
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Gutiérrez Hinojosa, S.J., Calvo, H., Moreno-Armendáriz, M.A., Duchanoy, C. (2020). Does Supervised Learning of Sentence Candidates Produce the Best Extractive Summaries?. In: Martínez-Villaseñor, L., Herrera-Alcántara, O., Ponce, H., Castro-Espinoza, F.A. (eds) Advances in Computational Intelligence. MICAI 2020. Lecture Notes in Computer Science(), vol 12469. Springer, Cham. https://doi.org/10.1007/978-3-030-60887-3_26
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