Two uses of anaphora resolution in summarization.

Abstract We propose a new method for using anaphoric information in Latent Semantic Analysis (LSA), and discuss its application to develop an LSA-based summarizer which achieves a significantly better performance than a system not using anaphoric information, and a better performance by the ROUGE measure than all but one of the single-document summarizers participating in DUC-2002. Anaphoric information is automatically extracted using a new release of our own anaphora resolution system, GUITAR, which incorporates proper noun resolution. Our summarizer also includes a new approach for automatically identifying the dimensionality reduction of a document on the basis of the desired summarization percentage. Anaphoric information is also used to check the coherence of the summary produced by our summarizer, by a reference checker module which identifies anaphoric resolution errors caused by sentence extraction

Reference resolution in multi-modal interaction: Preliminary observations

In this paper we present our research on multimodal interaction in and with virtual environments. The aim of this presentation is to emphasize the necessity to spend more research on reference resolution in multimodal contexts. In multi-modal interaction the human conversational partner can apply more than one modality in conveying his or her message to the environment in which a computer detects and interprets signals from different modalities. We show some naturally arising problems but do not give general solutions. Rather we decide to perform more detailed research on reference resolution in uni-modal contexts to obtain methods generalizable to multi-modal contexts. Since we try to build applications for a Dutch audience and since hardly any research has been done on reference resolution for Dutch, we give results on the resolution of anaphoric and deictic references in Dutch texts. We hope to be able to extend these results to our multimodal contexts later

Text as scene: discourse deixis and bridging relations

En este artículo se presenta un nuevo marco, “el texto como escena”, que establece las bases para la anotación de dos relaciones de correferencia: la deixis discursiva y las relaciones de bridging. La incorporación de lo que llamamos escenas textuales y contextuales proporciona unas directrices de anotación más flexibles, que diferencian claramente entre tipos de categorías generales. Un marco como éste, capaz de tratar la deixis discursiva y las relaciones de bridging desde una perspectiva común, tiene como objetivo mejorar el bajo grado de acuerdo entre anotadores obtenido por esquemas de anotación anteriores, que son incapaces de captar las referencias vagas inherentes a estos dos tipos de relaciones. Las directrices aquí presentadas completan el esquema de anotación diseñado para enriquecer el corpus español CESS-ECE con información correferencial y así construir el corpus CESS-Ancora.This paper presents a new framework, “text as scene”, which lays the foundations for the annotation of two coreferential links: discourse deixis and bridging relations. The incorporation of what we call textual and contextual scenes provides more flexible annotation guidelines, broad type categories being clearly differentiated. Such a framework that is capable of dealing with discourse deixis and bridging relations from a common perspective aims at improving the poor reliability scores obtained by previous annotation schemes, which fail to capture the vague references inherent in both these links. The guidelines presented here complete the annotation scheme designed to enrich the Spanish CESS-ECE corpus with coreference information, thus building the CESS-Ancora corpus.This paper has been supported by the FPU grant (AP2006-00994) from the Spanish Ministry of Education and Science. It is based on work supported by the CESS-ECE (HUM2004-21127), Lang2World (TIN2006- 15265-C06-06), and Praxem (HUM2006- 27378-E) projects

Keyword Detection in Text Summarization

Summarization is the process of reducing a text document in order to create a summary that retains the most important points of the original document. As the problem of information overload has grown, and as the quantity of data has increased, so has interest in automatic summarization. Extractive summary works on the given text to extract sentences that best convey the message hidden in the text. Most extractive summarization techniques revolve around the concept of indexing keywords and extracting sentences that have more keywords than the rest. Keyword extraction usually is done by extracting important words having a higher frequency than others, with stress on important. However the current techniques to handle this importance include a stop list which might include words that are critically important to the text. In this thesis, I present a work in progress to define an algorithm to extract truly significant keywords which might have lost its significance if subjected to the current keyword extraction algorithms