Extractive and Abstractive Summarization for Hindi Text using Hierarchical Clustering

Abstract

Text Summarization is a widely researched and successful area of Natural Language Processing application. However, it remains limited to established languages such as English, French, etc. In this paper, we propose and compare extractive and abstractive summarization techniques for Hindi text documents. For either summarization, we first propose ward hierarchical agglomerative clustering. This is followed by the PageRank algorithm for extractive summarization while in abstractive summarization, we present an approach based on multi-sentence compression which only requires a POS tagger to generate Hindi text summaries.