In automatic text summarization, the most relevant parts of a document are extracted and put together in a non-redundant summary that is shorter than the original document. A more advanced form of summarization is multi-text summarization where several texts are condensed into one summary. As the amount of information on the Internet grows abundantly, it is difficult to select relevant informat...

The goal of automatic text summarization is to reduce the size of a document while preserving its content. We investigate a summarization method which uses not only statistical features but also the contextual meaning of documents by using lexical clustering. We present a new method to compute lexical cluster in a text without high cost knowledge resources; the WordNet thesaurus. Summarization ...

In this paper, we attack the problem of forming extracts for text summarization. Forming extracts involves selecting the most representative and significant sentences from the text. Our method takes advantage of the lexical cohesion structure in the text in order to evaluate significance of sentences. Lexical chains have been used in summarization research to analyze the lexical cohesion struct...

Summarizing opinions expressed in online forums can potentially benefit many people. However, special characteristics of this problem may require changes to standard text summarization techniques. In this work, we present our initial attempt at extractive summarization of opinionated online forum threads. Given the nature of user generated content in online discussion forums, we hypothesize tha...

In some previous work, we have presented a software tool for experimenting with well known methods for text summarization. The methods offered are belonging to the extractive summarization direction. These methods do not understand the meaning in order to condense the text but simply extract a subset of the original sentences which are the most (promising as being) relevant for expressing short...

Text mining is nothing but extracting useful information from text. The information to be extracted is explicitly stated in the text. Text mining can be applied in various domains like medical, economical, etc. Medical text extraction can be done through medical patient reports, online medical journals, online health communities, etc. Text mining includes different tasks like classification, su...

In the automatic summarisation of written texts, direct speech is usually deemed unsuitable for inclusion in important sentences. This is due to the fact that humans do not usually include such quotations when they create summaries. In this paper, we argue that despite generally negative attitudes, direct speech can be useful for summarisation and ignoring it can result in the omission of impor...

Text summarization is machine based generation of a shortened version of a text. The summary should be a non-redundant extract from the original text. Most researches of text summarization use sentence extraction instead of abstraction to produce a summary. Extraction is depending mainly on sentences that already contained in the original input, which makes it more accurate and more concise. Wh...

While the field of Information Retrieval originally had the search for the most relevant documents in mind, it has become increasingly clear that in many instances, what the user wants is a piece of coherent information, derived from a set of relevant documents and possibly other sources. Reducing relevant documents, passages, and sentences to their core is the task of text summarization or inf...