Automatic Text Document Summarization Using Graph Based Centrality Measures on Lexical Network

This article proposes a new concept of Lexical Network for Automatic Text Document Summarization. Instead of a number of chains, the authors are getting a network of sentences which is called as Lexical Network termed as LexNetwork. This network is created between sentences based on different lexical and semantic relations. In this network, a node is representing sentences and edges are representing strength between two sentences. Strength means the number of relations present between the two sentences. The importance of the sentences is decided based on different centrality measures and extracted for the summary. WSD is done with Simple Lesk technique, and Cosine-Similarity threshold (Ɵ, TH) is used as post processing task. In this article, the authors are suggesting that a Cosine similarity threshold 10% is better vs. 5%, and an Eigen-Value based centrality measure is better for summarization process. At last for comparison, they are using Semantrica-Lexalytics System.