A Hybrid Approach for Multi-document Text Summarization

Text summarization has been a long studied topic in the field of natural language processing. There have been various approaches for both extractive text summarization as well as abstractive text summarization. Summarizing texts for a single document is a methodical task. But summarizing multiple documents poses as a greater challenge. This thesis explores the application of Latent Semantic Analysis, Text-Rank, Lex-Rank and Reduction algorithms for single document text summarization and compares it with the proposed approach of creating a hybrid system combining each of the above algorithms, individually, with Restricted Boltzmann Machines for multi-document text summarization and analyzing how all the approaches perform

Text Summarization Techniques: A Brief Survey

In recent years, there has been a explosion in the amount of text data from a variety of sources. This volume of text is an invaluable source of information and knowledge which needs to be effectively summarized to be useful. In this review, the main approaches to automatic text summarization are described. We review the different processes for summarization and describe the effectiveness and shortcomings of the different methods.Comment: Some of references format have update

Mixed-source multi-document speech-to-text summarization

Speech-to-text summarization systems usually take as input the output of an automatic speech recognition (ASR) system that is affected by issues like speech recognition errors, disfluencies, or difficulties in the accurate identification of sentence boundaries. We propose the inclusion of related, solid background information to cope with the difficulties of summarizing spoken language and the use of multi-document summarization techniques in single document speech- to-text summarization. In this work, we explore the possibilities offered by pho- netic information to select the background information and conduct a perceptual evaluation to better assess the relevance of the inclusion of that information. Results show that summaries generated using this approach are considerably better than those produced by an up-to-date latent semantic analysis (LSA) summarization method and suggest that humans prefer summaries restricted to the information conveyed in the input source.info:eu-repo/semantics/publishedVersio