Segmenting broadcast news streams using lexical chains

In this paper we propose a course-grained NLP approach to text segmentation based on the analysis of lexical cohesion within text. Most work in this area has focused on the discovery of textual units that discuss subtopic structure within documents. In contrast our segmentation task requires the discovery of topical units of text i.e. distinct news stories from broadcast news programmes. Our system SeLeCT first builds a set of lexical chains, in order to model the discourse structure of the text. A boundary detector is then used to search for breaking points in this structure indicated by patterns of cohesive strength and weakness within the text. We evaluate this technique on a test set of concatenated CNN news story transcripts and compare it with an established statistical approach to segmentation called TextTiling

Foreground and background text in retrieval

Our hypothesis is that certain clauses have foreground functions in text, while other clauses have background functions and that these functions are expressed or reflected in the syntactic structure of the clause. Presumably these clauses will have differing utility for automatic approaches to text understanding; a summarization system might want to utilize background clauses to capture commonalities between numbers of documents while an indexing system might use foreground clauses in order to capture specific characteristics of a certain document

SeLeCT: a lexical cohesion based news story segmentation system

In this paper we compare the performance of three distinct approaches to lexical cohesion based text segmentation. Most work in this area has focused on the discovery of textual units that discuss subtopic structure within documents. In contrast our segmentation task requires the discovery of topical units of text i.e., distinct news stories from broadcast news programmes. Our approach to news story segmentation (the SeLeCT system) is based on an analysis of lexical cohesive strength between textual units using a linguistic technique called lexical chaining. We evaluate the relative performance of SeLeCT with respect to two other cohesion based segmenters: TextTiling and C99. Using a recently introduced evaluation metric WindowDiff, we contrast the segmentation accuracy of each system on both "spoken" (CNN news transcripts) and "written" (Reuters newswire) news story test sets extracted from the TDT1 corpus

Text Segmentation Using Roget-Based Weighted Lexical Chains

In this article we present a new method for text segmentation. The method relies on the number of lexical chains (LCs) which end in a sentence, which begin in the following sentence and which traverse the two successive sentences. The lexical chains are based on Roget's thesaurus (the 1987 and the 1911 version). We evaluate the method on ten texts from the DUC 2002 conference and on twenty texts from the CAST project corpus, using a manual segmentation as gold standard

Summarizing Text Using Lexical Chains

The current technology of automatic text summarization imparts an important role in the information retrieval and text classification, and it provides the best solution to the information overload problem. And the text summarization is a process of reducing the size of a text while protecting its information content. When taking into consideration the size and number of documents which are available on the Internet and from the other sources, the requirement for a highly efficient tool on which produces usable summaries is clear. We present a better algorithm using lexical chain computation. The algorithm one which makes lexical chains a computationally feasible for the user. And using these lexical chains the user will generate a summary, which is much more effective compared to the solutions available and also closer to the human generated summary