TV News Story Segmentation Based on Semantic Coherence and Content Similarity

In this paper, we introduce and evaluate two novel approaches, one using video stream and the other using close-caption text stream, for segmenting TV news into stories. The segmentation of the video stream into stories is achieved by detecting anchor person shots and the text stream is segmented into stories using a Latent Dirichlet Allocation (LDA) based approach. The benefit of the proposed LDA based approach is that along with the story segmentation it also provides the topic distribution associated with each segment. We evaluated our techniques on the TRECVid 2003 benchmark database and found that though the individual systems give comparable results, a combination of the outputs of the two systems gives a significant improvement over the performance of the individual systems

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

Combining Visual Layout and Lexical Cohesion Features for Text Segmentation

We propose integrating features from lexical cohesion with elements from layout recognition to build a composite framework. We use supervised machine learning on this composite feature set to derive discourse structure on the topic level. We demonstrate a system based on this principle and use both an intrinsic evaluation as well as the task of genre classification to assess its performance

Integrating Prosodic and Lexical Cues for Automatic Topic Segmentation

We present a probabilistic model that uses both prosodic and lexical cues for the automatic segmentation of speech into topically coherent units. We propose two methods for combining lexical and prosodic information using hidden Markov models and decision trees. Lexical information is obtained from a speech recognizer, and prosodic features are extracted automatically from speech waveforms. We evaluate our approach on the Broadcast News corpus, using the DARPA-TDT evaluation metrics. Results show that the prosodic model alone is competitive with word-based segmentation methods. Furthermore, we achieve a significant reduction in error by combining the prosodic and word-based knowledge sources.Comment: 27 pages, 8 figure

Détection de la cohésion lexicale par voisinage distributionnel : application à la segmentation thématique

prix du meilleur articleNational audienceThe present work takes place within the Voiladis project (Lexical neighborhood for discourse analysis), whose purpose is to exploit lexical cohesion markers in the study of various discursive phenomena. We want to show the relevance of a distribution-based lexical resource to locate interesting relations between lexical items in a text. We call "neighbors" lexical items that share a significant number of syntactic contexts in a given corpus. In order to evaluate the usefulness of such a resource, we address the task of topical segmentation of text, which generally makes use of some kind of lexical relations. We discuss here the importance of the particular resource used for the task of text segmentation. Using a system inspired by [Hearst 1997], we show that lexical neighbors provide better results than a classical resource.Cette étude s'insère dans le projet VOILADIS (VOIsinage Lexical pour l'Analyse du DIScours), qui a pour objectif d'exploiter des marques de cohésion lexicale pour mettre au jour des phénomènes discursifs. Notre propos est de montrer la pertinence d'une ressource, construite par l'analyse distributionnelle automatique d'un corpus, pour repérer les liens lexicaux dans les textes. Nous désignons par "voisins" les mots rapprochés par l'analyse distributionnelle sur la base des contextes syntaxiques qu'ils partagent au sein du corpus. Pour évaluer la pertinence de la ressource ainsi créée, nous abordons le problème du repérage des liens lexicaux à travers une application de TAL, la segmentation thématique. Nous discutons l'importance, pour cette tâche, de la ressource lexicale mobilisée ; puis nous présentons la base de voisins distributionnels que nous utilisons ; enfin, nous montrons qu'elle permet, dans un système de segmentation thématique inspiré de [Hearst 1997], des performances supérieures à celles obtenues avec une ressource traditionnelle