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.

Topic-based Multi-document Summarization using Differential Evolution forCombinatorial Optimization of Sentences

Abstract: This paper describes a method of multidocument summarization with evolutionary computation. In automatic document summarization, the method to make a summary by finding the best combination of important sentences in target documents is popular approach. To find the best combination of sentences, explicit solution techniques such as integer linear programming, branch and bound method, and so on are usually adopted. However, there is a problem with them in terms of calculation efficiency. So, we apply evolutionary computation, especially differential evolution which is regarded as a method having a good feature in terms of calculation cost to obtain a reasonable quasi-optimum solution in real time, to the problem of combinatorial optimization of important sentences. Moreover, we consider latent topics in deciding the importance of a sentence, and define three fitness functions to compare the results. As a result, we have confirmed that our proposed methods reduced the calculation time necessary to make a summary considerably, although precision is more worse than the method with an explicit solution technique.

Word Summarization from a Paragraph Using Word Sense Disambiguation

Abstract: Now-a-days, with the increasing demand of the internet there is a huge volumes of information available on the internet. Users find it difficult to get information precisely. There should be a system from where users get main content instead of lengthy, redundant information. Text summarization is one of the techniques where we can get compressed version of original document. But so many problems realized in text summarizers such as redundancy, ambiguity (such as wrong spellings), and meaningless information. After having such problems in text summarization we have made a summarizer where a summary might not contain same text explicitly present in the original but still it will provide meaningful summary with no redundancy and ambiguity.

Multi-document Summarization and Opinion Mining Using Stack Decoder Method and Neural Networks

Abstract: Availability of data is not the foremost concern today; it is the extraction of relevant information from that data which requires the aid of technology. It is to help millions of users arrive at the desired information as quickly and effortlessly as possible. Document summarization and opinion-based document classification can effectively resolve the well-known problem of information overload on the Web. Summarization is about finding the perfect subset of data which holds the information of the entire set. In this paper, first we studied and evaluated three methods of generating summaries of multiple documents, namely, K-means clustering, novel-graph formulation method, and the stack decoder algorithm. The performance analysis emphasized on time, redundancy and coverage of the main content, was conducted along with the comparison between respective ROUGE scores. Next, hybrid architecture was proposed using a Stack decoder algorithm for creating automated summaries for multiple documents of similar kind, which were used as the dataset for analysis by a recursive neural tensor network to mine opinions of all the documents. The cross-validation of the generated summaries was done by comparing the polarity of summaries with their corresponding input documents. Finally, the results of opinion mining of each summary were compared with its corresponding documents and were found to be similar with few variations.

Single Document Summarization Using Natural Language Processing

Abstract: The need for text summarization is crucial as we enter the era of in­ formation overload. However, the current implementations are specific to a domain or a genre of the source document. In this paper, we discuss an algo­ rithm for text summarization which is independent of domain and document source. This algorithm creates text summaries by analyzing the logical struc­ ture of the sentences. Sentences are parsed and important relationships are identified, stored in the form of a graph, thus graph corresponding to each sen­ tence in the document is generated and merged to form graph of the document, now this graph is clustered into sub-graphs which represent the different topics in the document. Then a graph scoring algorithm scores the graph, and helps in extracting the important sentences towards summary. To increase the coher­ ence of the summary, the pool of extracted sentences undergoes some transfor­ mation in a specified order, resulting in final sentences that form the summary of the document.

Automatic Text Summarization for Web Pages on Internet

Abstract: This paper introduces automatic text summarization on Internet.Firstly,some main approaches to automatic text summarization are presented.Second,it discusses the new demands of automatic summarization for text on Internet and some related information.It also describes the process of text summarization on Internet and evaluation of it.Lastly,it draws a conclusion and prospect on the research of auto text summarization on Internet.