Word Embedding-based Text Processing for Comprehensive Summarization and Distinct Information Extraction

Xiangpeng Wan, Hakim Ghazzai, Yehia Massoud

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

In this paper, we propose two automated text processing frameworks specifically designed to analyze online reviews. The objective of the first framework is to summarize the reviews dataset by extracting essential sentence. This is performed by converting sentences into numerical vectors and clustering them using a community detection algorithm based on their similarity levels. Afterwards, a correlation score is measured for each sentence to determine its importance level in each cluster and assign it as a tag for that community. The second framework is based on a question-answering neural network model trained to extract answers to multiple different questions. The collected answers are effectively clustered to find multiple distinct answers to a single question that might be asked by a customer. The proposed frameworks are shown to be more comprehensive than existing reviews processing solutions.
Original languageEnglish (US)
Title of host publication2020 IEEE Technology and Engineering Management Conference, TEMSCON 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781728142241
DOIs
StatePublished - Jun 1 2020
Externally publishedYes

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