With the exponential growth of textual information on web and in multimedia, query-focused multi-document summarization (QFMS) has emerged as a critical research area. QFMS aims to generate concise summaries that address user queries satisfy their needs. This paper provides comprehensive survey state-of-the-art approaches QFMS, focusing specifically graph-based clustering-based methods. Each ap...

Automatic text summarization aims to cut down readers’ time and cognitive effort by reducing the content of a document without compromising on its essence. Ergo, informativeness is prime attribute summary generated an algorithm, selecting sentences that capture essence primary goal extractive summarization. In this paper, we employ Shannon’s entropy sentences. We Non-negative Matrix Factorizati...

In this research work we have proposed two-tier architecture for document summarization. This architecture minimizes the redundancy and boosts the information relevancy in the summary by applying Probabilistic Latent Semantic Analysis (PLSA) at two levels. It also enhances the summarizer’s speed by using Incremental Expectation Maximization algorithm for PLSA learning rather than Expectation Ma...

This paper presents a survey of recent extractive query-based summarization techniques. We explore approaches for single document and multidocument summarization. Knowledge-based and machine learning methods for choosing the most relevant sentences from documents with respect to a given query are considered. Further, we expose tailored summarization techniques for particular domains like medica...

Single document summarization is the task of producing a shorter version of a document while preserving its principal information content. In this paper we conceptualize extractive summarization as a sentence ranking task and propose a novel training algorithm which globally optimizes the ROUGE evaluation metric through a reinforcement learning objective. We use our algorithm to train a neural ...

We participated in the Document Understanding Conference 2002 (DUC-2002) in order to confirm the effectiveness of our summarization system based on an important sentence extraction technique. Our system employs the machine learning algorithm, Support Vector Machines, to classify a sentence into an important or an unimportant sentence. The result of the Single-Document Summarization task shows t...

This paper deals with our past and recent research in text summarization. We went from single-document summarization through multidocument summarization to update summarization. We describe the development of our summarizer which is based on latent semantic analysis (LSA). The classical LSA-based summarization model was improved by Iterative Residual Rescaling. We propose the update summarizati...

Hierarchical summarization technique summarizes a large document based on the hierarchical structure and salient features of the document. Previous study has shown that hierarchical summarization is a promising technique which can effectively extract the most important information from the source document. Hierarchical summarization has been extended to summarization of multiple documents. Thre...