A Framework for Multi-document Extractive Summarization of Reviews with Aspect-based Sentiment Analysis - Oliveira, André; Costa, Anna; Hruschka, Eduardo (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), 2020)
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.