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

arXiv: https://arxiv.org/abs/2004.09719 · HTML: https://mlsanity.com/paper/2004.09719/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2020.

## Built on by (most cited first, 1 of 1)

- [Topic Modeling and Progression of American Digital News Media During the Onset of the COVID-19 Pandemic](https://mlsanity.com/paper/2106.09572/) (2021)

## Abstract (arXiv)

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.