We discuss text summarization in terms of maximum coverage problem and its variant. We explore some decoding algorithms including the ones never used in this summarization formulation, such as a greedy algorithm with performance guarantee, a randomized algorithm, and a branch-andbound method. On the basis of the results of comparative experiments, we also augment the summarization model so that it takes into account the relevance to the document cluster. Through experiments, we showed that the augmented model is superior to the best-performing method of DUC’04 on ROUGE-1 without stopwords. 1

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Wikipedia provides a wealth of knowledge, where the first sentence, infobox (and relevant sentences), and even the entire document of a wiki article could be considered as diverse versions of summaries (definitions) of the target topic. We explore how to generate a series of summaries with various lengths based on them. To obtain more reliable associations between sentences, we introduce wiki concepts according to the internal links in Wikipedia. In addition, we develop an extended document concept lattice model to combine wiki concepts and non-textual features such as the outline and infobox. The model can concatenate representative sentences from non-overlapping salient local topics for summary generation. We test our model based on our annotated wiki articles which topics come from TREC-QA 2004-2006 evaluations. The results show that the model is effective in summarization and definition QA. 1

Resumen: El objetivo de nuestra investigación es el de generar resúmenes de películas a partir de textos colaterales, capturando el contenido semántico, estructura narrativa y líneas clave de los diálogos de la película. Nuestra hipótesis es que se pueden generar de forma eficiente resúmenes en texto de películas mediante el empleo de técnicas de resumen automático sobre textos colaterales: subtítulos, descripciones del audio y guiones de postproducción. En caso de disponer de códigos de tiempo, entonces podemos generar también resúmenes en vídeo a partir de dichos resúmenes en texto. En este estudio inicial construimos los resúmenes seleccionando las diez tomas de la película original que contienen la mayor proporción de palabras clave. Definimos las palabras clave de dos formas: como palabras de frecuencia media, ya que son las palabras de frecuencia media en un texto las que contienen la mayor parte de la información acerca del contenido de dicho texto; y como entidades nombradas derivadas del reparto de la película. Ye et al. (2007) sostienen que la calidad de un resumen puede evaluarse en base a cuántos de los conceptos principales del texto original se conservar en el resumen. Hemos comprobado que esta aproximación a la evaluación de resúmenes obtiene resultados más favorables que la

help? Single document summarization Simulate the work of intelligence analyst Judge if a document is relevant to a topic of interest “Summaries as short as 17 % of the full text length speed up decision making twice, with no significant degradation in accuracy.” “Query-focused summaries enable users to find more relevant documents more accurately, with less need to consult the full text of the document.” [Mani et al., 2002] 7 Motivation: multi-document summarization helps in compiling and presenting Reduce search time, especially when the goal of the user is to find as much information as possible about a given topic � Writing better reports, finding more relevant information, quicker Cluster similar articles and provide a multi-document summary of the similarities Single document summary of the information unique to