Improved Algorithms for Document Classification &Query-based Multi-Document Summarization

Abstract

With an excess of information in recent times, sound information retrieval is the need of the hour. Document Classification, where a document is classified as being under one of a number of predefined categories, is the foundation of an efficient and effective Information Retrieval system. Once information has been retrieved, the next step is unearthing the relevant and essential information. Query-based Multi- Document Summarization will do just that. In this paper, we analyze the different variants of the k Nearest Neighbors (kNN) Classification Algorithm and from them design the CAST Algorithm for Classification, which, as precision and recall results will show, performs better in most cases. For document summarization, we analyze and improvise on a Hypergraph based algorithm. Further, we design and describe the CAST Algorithm for Summarization and show that it performs well for Query-based Multi-Document Summarization.