Multi-document summarization

Abstract: We present a method for summarizing broadcast news that is not affected by word errors in an automatic speech recognition transcription, using information about the structure of the news program. We construct a directed graphical model to represent the probability distribution and dependencies among the structural features which we train by finding the values of parameters of the conditional probability tables. We then rank segments of the test set and extract the highest ranked ones as a summary. We present the procedure and preliminary test results.

Abstract: In this paper, we propose text summarization method that creates text summary by definition of the relevance score of each sentence and extracting sentences from the original documents. While summarization this method takes into account weight of each sentence in the document. The essence of the method suggested is in preliminary identification of every sentence in the document with characteristic vector of words, which appear in the document, and calculation of relevance score for each sentence. The relevance score of sentence is determined through its comparison with all the other sentences in the document and with the document title by cosine measure. Prior to application of this method the scope of features is defined and then the weight of each word in the sentence is calculated with account of those features. The weights of features, influencing relevance of words, are determined using genetic algorithms.

Abstract: Text summarization is a process to produce an abstract or a summary by selecting significant portion of the information from one or more texts. In an automatic text summarization process, a text is given to the computer and the computer returns a shorter less redundant extract or abstract of the original text(s). Many techniques have been developed for summarizing English text(s). But, a very few attempts have been made for Bengali text summarization. This paper presents a method for Bengali text summarization which extracts important sentences from a Bengali document to produce a summary.

Abstract: In this paper, we propose the use of multido ument summarization as a post-pro essing step in do ument retrieval We examine the use of the summary as a repla ement to the standard ranked list The form of the summary is novel be ause it has both informative and indi ate elements, designed to help di erent users perform their tasks better Our summary uses the do uments' topi al stru ture as a ba kbone for its own stru ture, as it was deemed the most useful do ument feature in our study of a orpus of summaries