This paper describes our participation at TAC 2008 in all the three tracks. For the Summarization Track we introduced two major features. First, a feature based on Information Loss if we don’t pick a particular sentence. Second, a language modeling extension that boosts novel terms and penalizes stale terms. During our post-TAC analysis we observed that a simple sentence position based summariz...

The recent advancements in big data and natural language processing (NLP) have necessitated proficient text mining (TM) schemes that can interpret analyze voluminous textual data. Text summarization (TS) acts as an essential pillar within recommendation engines. Despite the prevalent use of abstractive techniques TS, anticipated shift towards a graph-based extractive TS (ETS) scheme is becoming...

To summarize a text means to compress the text source into a shorter text in a way that the informational content is kept the same. With regard to the irregular volume of information available on the internet, manual summarization of huge volume of information by humans will be very arduous and difficult. There have been many activities in the field of automatic summarization so far. However, a...

In this paper, we introduce a novel graph based technique for topic based multidocument summarization. We transform documents into a bipartite graph where one set of nodes represents entities and the other set of nodes represents sentences. To obtain the summary we apply a ranking technique to the bipartite graph which is followed by an optimization step. We test the performance of our method o...

RDF is the data model of choice for Semantic Web applications. RDF graphs are often large and have heterogeneous, complex structure. Graph summaries are compact structures computed from the input graph; they are typically used to simplify users’ experience and to speed up graph processing. We introduce a formal RDF summarization framework, based on graph quotients and RDF node equivalence; our ...

This thesis describes an automatic text summarization system based on graph-theory. Summaries are useful indicators of the document content. Traditionally summaries are created by humans by reading the text and identifying the important points in the text. So called information-overload makes such manual work very difficult (if not impossible). Many attempts have been made to automate the summa...

Summarization is a widespread method for handling very large graphs. The task of structural graph summarization to compute concise but meaningful synopsis the key information graph. As summaries may be used many different purposes, there no single concept or model summaries. We have studied existing large-scale (semantic) Despite their concepts and we found commonalities in structures they capt...

This paper presents a survey of graph based methods for word sense induction and disambiguation. Many areas of Natural Language Processing like Word Sense Disambiguation (WSD), text summarization, keyword extraction make use of Graph based methods. The very idea behind graph based approach is to formulate the problems in graph setting and apply clustering to obtain a set of clusters (senses). T...

Text summarization endeavors to produce a summary version of a text, while maintaining the original ideas. The textual content on the web, in particular, is growing at an exponential rate. The ability to decipher through such massive amount of data, in order to extract the useful information, is a major undertaking and requires an automatic mechanism to aid with the extant repository of informa...