We discuss technology to help a person monitor changes in news coverage over time. We define temporal summaries of news stories as extracting a single sentence from each event within a news topic, where the stories are presented one at a time and sentences from a story must be ranked before the next story can be considered. We explain a method for evaluation, and describe an evaluation corpus that we have built. We also propose several methods for constructing temporal summaries and evaluate their effectiveness in comparison to degenerate cases. We show that simple approaches are effective, but that the problem is far from solved. Keywords Summarization, Experimental Design and Metrics 1.

This paper describes the development of a prototype system to answer questions by selecting sentences from the documents in which the answers occur. After parsing each sentence in these documents, databases are constructed by extracting relational triples from the parse output. The triples consist of discourse entities, semantic relations, and the governing words to which the entities are bound in the sentence. Database triples are also generated for the questions. Question-answering consists of matching the question database records with the records for the documents. The prototype system was developed specifically to respond to the TREC-8 Q&A track, with an existing parser and some existing capability for analyzing parse output. The system was designed to investigate the viability of using structural information about the sentences in a document to answer questions. The CL Research system achieved an overall score of 0.281 (i.e., on average, providing a sentence containing a correct...

Objective: The aim of this paper is to survey the recent work in medical documents summarization. Background: During the last decade, documents summarization got increasing attention by the AI research community. More recently it also attracted the interest of the medical research community as well, due to the enormous growth of information that is available to the physicians and researchers in medicine, through the large and growing number of published journals, conference proceedings, medical sites and portals on the World Wide Web, electronic medical records, etc. Methodology: This survey gives first a general background on documents summarization, presenting the factors that summarization depends upon, discussing evaluation issues and describing briefly the various types of summarization techniques. It then examines the characteristics of the medical domain through the different types of medical documents. Finally, it presents and discusses the summarization techniques used so far in the medical domain, referring to the corresponding systems and their characteristics. Discussion and Conclusions: The paper discusses thoroughly the promising paths for future research in medical documents summarization. It mainly focuses on the issue of scaling to large collections of documents in various languages and from different media, on personalization issues, on portability to new sub-domains, and on the integration of summarization technology in practical applications.

Topic-focused multi-document summarization aims to produce a summary biased to a given topic or user profile. This paper presents a novel extractive approach based on manifold-ranking of sentences to this summarization task. The manifold-ranking process can naturally make full use of both the relationships among all the sentences in the documents and the relationships between the given topic and the sentences. The ranking score is obtained for each sentence in the manifold-ranking process to denote the biased information richness of the sentence. Then the greedy algorithm is employed to impose diversity penalty on each sentence. The summary is produced by choosing the sentences with both high biased information richness and high information novelty. Experiments on DUC2003 and DUC2005 are performed and the ROUGE evaluation results show that the proposed approach can significantly outperform existing approaches of the top performing systems in DUC tasks and baseline approaches. 1

This paper demonstrates the usefulness of summaries in an extrinsic task of relevance judgment based on a new method for measuring agreement, Relevance-Prediction, which compares subjects ' judgments on summaries with their own judgments on full text documents. We demonstrate that, because this measure is more reliable than previous gold-standard measures, we are able to make stronger statistical statements about the benefits of summarization. We found positive correlations between ROUGE scores and two different summary types, where only weak or negative correlations were found using other agreement measures. However, we show that ROUGE may be sensitive to the choice of summarization style. We discuss the importance of these results and the implications for future summarization evaluations.

Objectives: We present the summarization system in the PERSIVAL medical digital library. Although we discuss the context of our summarization research within the PERSIVAL platform, the primary focus of this article is on strategies to define and generate customized summaries.

Information of interest to users is often distributed over a set of documents. Users can specify their request for information as a query/topic – a set of one or more sentences or questions. Producing a good summary of the relevant information relies on understanding the query and linking it with the associated set of documents. To “understand ” the query we expand it using encyclopedic knowledge in Wikipedia. The expanded query is linked with its associated documents through spreading activation in a graph that represents words and their grammatical connections in these documents. The topic expanded words and activated nodes in the graph are used to produce an extractive summary. The method proposed is tested on the DUC summarization data. The system implemented ranks high compared to the participating systems in the DUC competitions, confirming our hypothesis that encyclopedic knowledge is a useful addition to a summarization system. 1

Inference tasks in computational semantics have mostly been tackled by means of first-order theorem proving tools. While this is an important and welcome development, it has some inherent limitations. First, generating first-order logic representations of natural language documents is hampered by the lack of efficient and sufficiently robust NLP tools. Second, the computational costs of deploying first-order logic theorem proving tools in realworld situations may be prohibitive. And third, the strict yes/no decisions delivered by such tools are not always appropriate. In this paper we report on an approach to inference in semantics that works on very minimal representations which can easily be generated for arbitrary domains. Moreover, our approach is computationally efficient, and provides graded outcomes instead of strict yes/no decisions. Our approach is fully implemented, and a preliminary evaluation of the approach is discussed in the paper. 1

This paper describes an affinity graph based approach to multi-document summarization. We incorporate a diffusion process to acquire semantic relationships between sentences, and then compute information richness of sentences by a graph rank algorithm on differentiated intra-document links and inter-document links between sentences. A greedy algorithm is employed to impose diversity penalty on sentences and the sentences with both high information richness and high information novelty are chosen into the summary. Experimental results on task 2 of DUC 2002 and task 2 of DUC 2004 demonstrate that the proposed approach outperforms existing state-of-theart systems. 1

Since 1995, we have been researching the application of ubiquitous computing technology to support the automated capture of live university lectures so that students and teachers may later access them. With virtually no additional effort beyond that which lecturers already expend on preparing and delivering a lecture, we are able to create a repository, or digital library, of rich educational experiences that is constantly growing. The resulting archive includes a heterogeneous mix of materials presented in lectures. In this paper, we discuss access issues for this digital library that cover short-term and long-term use of the repository. While our efforts have focussed quite a bit on short-term access that occurs over the duration of a course, it is clear that significant value is added to the archive as it is tuned for long-term use. These long-term access issues for an experiential digital library have not yet been addressed, and we highlight some of those challenges in this paper. 1.