The division of a protein interaction network into biologically meaningful modules can aid with automated detection of protein complexes and prediction of biological processes and can uncover the global organization of the cell. We propose the use of a graph summarization (GS) technique, based on graph compression, to cluster protein interaction graphs into biologically relevant modules. The me...

A user’s information need, normally represented as a search query, can be satisfied by creating a query focused coherent and readable summary, by fusing the relevant parts of information from multiple documents. While aggregating the information from multiple documents, the quality of the summary is improved by eliminating redundant information from the document set. In this paper, we focus on ...

In this paper we introduce two systems RSumm and CNSumm, which are multi-document summarizers based on the adaptation of the singledocument relationship map and complex network methods, which represent texts as graphs and select sentences to compose the summary by using different graph traversing strategies and complex networks measures.

Today, with rapid growth of the World Wide Web and creation of Internet sites and online text resources, text summarization issue is highly attended by various researchers. Extractive-based text summarization is an important summarization method which is included of selecting the top representative sentences from the input document. When, we are facing into large data volume documents, the extr...

Scientific workflow management systems (SWfMS) are powerful tools in the automation of scientific experiments. Several workflow executions are necessary to accomplish one scientific experiment. Data provenance, typically collected by SWfMS during workflow execution, is important to understand, reproduce and analyze scientific experiments. Provenance is about data derivation, thus it is typicall...

The present paper describes the development of a query focused multi-document automatic summarization. A graph is constructed, where the nodes are sentences of the documents and edge scores reflect the correlation measure between the nodes. The system clusters similar texts having related topical features from the graph using edge scores. Next, query dependent weights for each sentence are adde...

Many massive web and communication network applications create data which can be represented as a massive sequential stream of edges. For example, conversations in a telecommunication network or messages in a social network can be represented as a massive stream of edges. Such streams are typically very large, because of the large amount of underlying activity in such networks. An important app...

We consider the problem of answering complex questions that require inferencing and synthesizing information from multiple documents and can be seen as a kind of topicoriented, informative multi-document summarization. The stochastic, graph-based method for computing the relative importance of textual units (i.e. sentences) is very successful in generic summarization. In this method, a sentence...

For the update summarization task of TAC 2009, we submitted two runs using two different methods. The first one is manifold ranking method, which models all sentences as a graph. The topic description is deemed as the only labeled node and assigned with an initial score, then the scores of all the sentences in the documents are learned by spreading the initial score on the graph. The second met...

This paper proposes an improved approach of summarization for spoken multi-party interaction, in which intra-speaker and inter-speaker topics are modeled in a graph constructed with topical relations. Each utterance is represented as a node of the graph and the edge between two nodes is weighted by the similarity between the two utterances, which is topical similarity evaluated by probabilistic...