Automatic Text Summarization (ATS) in Natural Language Processing has been an important task in Information Retrieval.
It compresses a document to create a summary that captures all the relevant and important information conveyed in the document.
This study explores Hypergraph for extractive text summarization of single documents.
Objective: This study explores a novel method of leveraging the property of domination in hypergraphs to generate an extractive summary and compare its performance with state of the art graph based methods.
Method:
Our work aims to generate an extractive summary by creating a sentence hypergraph where each sentence represents a node and the edge is a keyword or a named entity that contains the sentences in which it occurs.
We generate a hypergraph where each edge is a keyword or an important topic and the nodes are sentences containing those keywords.
Then we apply a greedy algorithm to find the dominating set of the hypergraph which will contain sentences that will form the extractive summary.