Multi-document summarization

About: Multi-document summarization is a research topic. Over the lifetime, 2270 publications have been published within this topic receiving 71850 citations.

Algebraic reduction in automatic text summarization – the state of the art

Abstract: Various kinds of information that is available on a topic electronically has abundantly increased over the past years. It has led the information highway to a situation called “information overload” problem. Automatic text summarization technique mainly addresses this issue by the extraction of a shortened version of information from texts written about the same topic. Several algebraic reduction methods are used to identify and extract the semantically important texts in a document to summarize it automatically. This paper attempts to provide a background study of the various classical methods proposed by researchers for automatic text summarization. Special focus is given to the most widely used algebraic methods called Singular Value Decomposition (SVD) and Non-negative Matrix Factorization (NMF). This work sheds more light on the application of SVD and NMF techniques on automatic text summarization. Attention is also devoted in this work to analyze the advantages and disadvantages of each approach.

Analysis of complex network methods for extractive automatic text summarization

Abstract: Automatic text summarization is an important research area in the domain of information systems. It aims to create a compressed version of documents, which should cover all the significant contents and overall meaning. In extractive text summarization, sentences are scored on various of features. A large number of features network based have been proposed by researchers in the past literatures. This paper reviews all the features that use metrics and concept of complex network for scoring sentences. The experiment results on single feature and combinations of various features we proposed are discussed. Quantitative and qualitative aspects were considered in our assessment performing on the DUC 2002 data sets.

Automatic text summarization with statistical and linguistic features using successive thresholds

Abstract: Text summarization is an emerging technique for finding out the summary of the text document. Text summarization is nothing but summarizing the content of given text document. Text summarization has got so uses such as Due to the massive amount of information getting increased on internet; it is difficult for the user to go through all the information available on web. Summarization techniques need to be used to reduce the user's time in reading the whole information available on web. In this paper, we propose an automatic text summarization technique using both linguistic and statistical features using successive threshold for finding the summary i.e important sentences from the given input text document. Here the sentences are selected for summary based on the weight of the sentence. The weight of the sentences is calculated based on the statistical and linguistic features. Our approach assigns scores to the sentences by weighting the features like term frequency, word occurrences, and noun weight, phrases etc. In our approach, the number of sentences present in our summary would be equal to the number of paragraphs present in a text document, which can be achieved by using our successive threshold approach.

Automatic text summarization based on semantic analysis approach for documents in Indonesian language

Abstract: Research about text summarization has been quite an interesting topic over the years, proven by numerous number of papers related with discussion of their studies such as approaches, challenges and trends. This paper's goal is to define a measurement for text summarization using Semantic Analysis Approach for Documents in Indonesian language. The applied measurement requires Indonesian version of WordNet which had been implemented roughly. The main idea of semantic analysis is to obtain the similarity between sentences by calculating the vector values of each sentence with the title. The need ofWordNet is to define the depth of each word as being computed for word similarity. Combining all required formulas and calculations, a compact and precise summarization is produced without depriving the gist information of certain document.

Subjective assessment of consumer video summarization

Abstract: The immediate availability of a vast amount of multimedia content has created a growing need for improvements in the field of content analysis and summarization. While researchers have been rapidly making contributions and improvements to the field, we must never forget that content analysis and summarization themselves are not the user's goals. Users' primary interests fall into one of two categories; they normally either want to be entertained or want to be informed (or both). Summarization is therefore just another tool for improving the entertainment value or the information gathering value of the video watching experience. In this paper, we first explore the relationship between the viewer, the interface, and the summarization algorithms. Through an understanding of the user's goals and concerns, we present means for measuring the success of the summarization tools. Guidelines for the successful use of summarization in consumer video devices are also discussed.