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.

Social media online opinion summarization using ensemble technique

Abstract: Today, the world is going towards web due to tremendous growth of Internet. People are taking a help of opinions from web for getting ideas to start a new business, improving its current business or taking knowledge about particular thing from different point of views. Opinions are not always true or beneficial since people write opinion based on its own behavior, emotions, experience which makes people confused by seeing various types of opinions and get failed to take right decision. Therefore, there are needs for summarization of such fraudulent opinions and has become great challenge in today's e-world. This paper proposed robust feature-based opinion summarization system based on weighting scheme and association rule after preprocessing techniques, also ensemble technique used for feature extraction and finally finding out the orientation of extracted features and then display the summary of reviews.

Information Transfer through Online Summarizing and Translation Technology

Abstract: Information access – presented in proper language, in understandable way, at the right time and right place can be of considerable importance. Information and communication technology, wrapping also human language technologies, can play important role in information transfer to the specific user. Translation technology along with summarizing technology has opened new possibilities and perspectives, requiring in the same time the critical opinion in information analysis. The main purpose of this research is to present the impact of text summarization and online machine translation tools on information transfer. The research was performed on texts taken from online newspapers in five do- mains (politics, news, sport, film and gastronomy) in English, German and Russian languages. The total of N=240 evaluations were analysed, performed by the same three evaluators. In the research three types of assignments were made. The first assignment was to evaluate machine-translated sentences at the sentence level for the three language pairs (English-Croatian, German-Croatian and Russian-Croatan). In the second task, the similar evaluation was performed, but at the whole text level. In the third assignment, which was related to information transfer, the evaluators were asked to evaluate the overall quality of the texts process in the pipe- lined process (online summarization and online machine translation) for English and German. Assessment was based on the finding the answers to the following questions – who, what, when, where, and how? The results were analysed by ANOVA, t-test and binary logistic regression.

Automatic summarization of Chinese and English parallel documents

Abstract: As a result of the rapid growth in Internet access, significantly more information has become available online in real time. However, there is not sufficient time for users to read large volumes of information and make decisions accordingly. The problem of information-overloading can be resolved through the application of automatic summarization. Many summarization systems for documents in different languages have been implemented. However, the performance of summarization system on documents in different languages has not yet been investigated. In this paper, we compare the result of fractal summarization technique on parallel documents in Chinese and English. The grammatical and lexical differences between Chinese and English have significant effect on the summarization processes. Their impact on the performances of the summarization for the Chinese and English parallel documents is compared.

Research and Implementation of Automatic Multi-Document Summarization System

Abstract: Automatic multi-document summarization is an outgrowth of single document summarization. A statistical approach to multi-document summarization is presented. It utilizes the semantic relevance between segments of documents. Text-tiling algorithm is implemented to break documents into semantic relevant segments. These segments are merged into some topic classes according to the semantic similarity by using clustering algorithm. The representative segments are extracted from topic classes to form the summarization result. By using real Chinese corpus, experimental results show the system' s effectiveness and suitability.

Web News Multi-document Summarization Based on Event Extraction

Abstract: State-of-the-art automatic summarization is based on text segment clustering to avoid redundancy defects in the traditional approachesBut some of the text segments in the web news are irrelevant to the subject,which affects the result of clustering and damages the conciseness of summarizationThis paper introduces the event extraction technology and proposes an event extraction based web news multi-document summarization methodFirstly,the method distinguishes event and non-event from the news through a binary classifierThen,the original documents' physical division based on paragraphs or sentences are transformed into event based content logical division through clusteringFinally,the summarization is derived from the extraction,taxis and embellishment of the major eventsExperimental results demonstrate the effectiveness of the proposed method,which improves summarization quality significantly