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

André Seidel Oliveira | Zendy; Anna Helena Reali Costa | Zendy; Eduardo R. Hruschka | Zendy

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

Author(s) -

André Seidel Oliveira,

Anna Helena Reali Costa,

Eduardo R. Hruschka

Publication year - 2020

Publication title -

anais do encontro nacional de inteligência artificial e computacional (eniac)

Language(s) - English

Resource type - Conference proceedings

ISSN - 2763-9061

DOI - 10.5753/eniac.2020.12152

Subject(s) - automatic summarization , computer science , sentiment analysis , relevance (law) , similarity (geometry) , information retrieval , natural language processing , topic model , artificial intelligence , image (mathematics) , political science , law

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.

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