A Framework for Multi-document Extractive Summarization of Reviews with Aspect-based Sentiment Analysis

  • André Oliveira Universidade de São Paulo
  • Anna Costa Universidade de São Paulo
  • Eduardo Hruschka Universidade de São Paulo


We propose an integrated framework, named Multi-Document Aspect-based Sentiment Extractive Summarization (MD-ASES for short), to automatically generate extractive review summaries based on aspects of a large database with reviews of items such as films, businesses, and companies. Such summaries are got by extracting a subset of sentences as they are in the reviews, based on some relevance criteria. In MD-ASES, initially sentences are grouped in terms of aspects identified as predominant in the reviews. Then, sentences are selected by the similarity of the sentiment expressed about a particular aspect to the overall sentiment of the dataset reviews. Our results show that MD-ASES can successfully preserve the average sentiment of the reviews while including the most important aspects in the summary.

Palavras-chave: Machine Learning, Text and Web mining, Natural Language Processing, Decision Support Systems, Data Science


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OLIVEIRA, André; COSTA, Anna; HRUSCHKA, Eduardo. A Framework for Multi-document Extractive Summarization of Reviews with Aspect-based Sentiment Analysis. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 17. , 2020, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2020 . p. 471-482. ISSN 2763-9061. DOI: https://doi.org/10.5753/eniac.2020.12152.