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 diff...

As a continuation of the summarization track of TAC 2010, the TAC 2011 summarization track pursues aspect-guided summarization. It is intended “to encourage a deeper linguistic (semantic) analysis of the source documents instead of relying only on document word frequencies to select important concepts”. The sustained interest in guided summarization marks a significant turn to semantically orie...

Multi-document summarization is a process of automatic generation of a compressed version of the given collection of documents. Recently, the graph-based models and ranking algorithms have been actively investigated by the extractive document summarization community. While most work to date focuses on homogeneous connecteness of sentences and heterogeneous connecteness of documents and sentence...

This paper describes the multi-document text summarization system NeATS. Using a simple algorithm, NeATS was among the top two performers of the DUC-01 evaluation.

We show that a simple procedure based on maximizing the number of informative content-words can produce some of the best reported results for multi-document summarization. We first assign a score to each term in the document cluster, using only frequency and position information, and then we find the set of sentences in the document cluster that maximizes the sum of these scores, subject to len...

This paper discusses an approach to topic-oriented multidocument summarization. It investigates the effectiveness of using additional information about the document set as a whole, as well as individual documents. We present NEO-CORTEX, a multi-document summarization system based on the existing CORTEX system. Results are reported for experiments with a document base formed by the NIST DUC-2005...

Library summarization is an effective way to accelerate the analysis of client code. However, information about the client is unknown at the library summarization, preventing complete summarization of the library. An existing approach utilizes tree-adjoining languages (TALs) to provide conditional summaries, enabling the summarization of a library under certain premises. However, the use of TAL...

Usually, presence of the same information in multiple documents is the main problem faced in effective information access. Instead of this redundant information thus accessed or retrieved, users are interested in retrieving information that addresses one or other several aspects. In such situation, text summarization proves to be very useful. Not only in Information retrieval, but it is an extr...

Text Summarization is condensing the source text into a shorter version preserving its information content and overall meaning. It is very difficult for human beings to manually summarize large documents of text. Text Summarization methods can be classified into extractive and abstractive summarization. An extractive summarization method consists of selecting important sentences, paragraphs etc...

Keyword search is a useful tool for exploring large RDF datasets. Existing techniques either rely on constructing a distance matrix for pruning the search space or building summarization from the RDF graphs for query processing. In this work, we show that existing techniques have serious limitations in dealing with realistic, large RDF graphs with tens of millions of triples. Furthermore, the e...