This paper addresses the classification of semantic relations between pairs of sentences extracted from a Dutch parallel corpus at the word, phrase and sentence level.

In this paper, we present approaches used in text summarization, showing how they can be adapted for speech summarization and where they fall short. Informal style and apparent lack of structure in speech mean that the typical approaches used for text summarization must be extended for use with speech. We illustrate how features derived from speech can help determine summary content within two ongoing summarization projects at Columbia University. 1.

Machine summaries can be improved by using knowledge about the cognitive status of news article referents. In this paper, we present an approach to automatically acquiring distinctions in cognitive status using machine learning over the forms of referring expressions appearing in the input.

We describe an experiment to determine the quality of different similarity metrics, with regard to redundancy removal. The three metrics under examination are WordNet distance, Cosine Similarity (Vector-space model) and Latent Semantic Indexing.

In this paper, we use the information redundancy in multilingual input to correct errors in machine translation and thus improve the quality of multilingual summaries. We consider the case of multidocument summarization, where the input documents are in Arabic, and the output summary is in English. Typically, information that makes it to a summary appears in many different lexical-syntactic forms in the input documents. Further, the use of multiple machine translation systems provides yet more redundancy, yielding different ways to realize that information in English. We demonstrate how errors in the machine translations of the input Arabic documents can be corrected by identifying and generating from such redundancy, focusing on noun phrases. 1

To my parents who greatly facilitated my return to graduate school and have supported me throughout. To Lee for enduring the seemingly endless process, the many long nights and the minimal amount of free time I’ve had. To Tim for all the Java help that I would have been lost without. To Chirstina for all her help editing my documents. And to Andrew for his patience, guidance and support. Without all of you I couldn’t have made it.

Advances in multimedia technologies have enabled the creation of huge archives of audio-video recordings of meetings, and there is burgeoning interest in developing meeting browsers to help users better leverage these archives. A recent study has shown that extractive summaries provide a more efficient way of navigating meeting content than simply reading through the transcript and using the audio-video record, or navigating via keyword search [15]. The extractive summary technique identifies informative dialogue acts to generate general purpose summaries. These summaries can still be lengthy. Recently, we have developed a decisionfocused summarization system that presents only 1-2 % of the recordings related to decision making. In this paper, we describe a task-based evaluation in which we compare the decision-focused summaries to the general purpose summaries. Our results indicate that the more focused summaries help users perform the decision debriefing task more effectively and improve perceived efficiency. In addition, this study also investigates the effect of automatic summaries and transcription on task effectiveness, report quality, and users ’ perceptions of task success. Author Keywords Meeting browser, automatic summarization, multimedia information

Classification of semantic relations by humans and machines ∗ This paper addresses the classification of semantic relations between pairs of sentences extracted from a Dutch parallel corpus at the word, phrase and sentence level. We first investigate the performance of human annotators on the task of manually aligning dependency analyses of the respective sentences and of assigning one of five semantic relations to the aligned phrases (equals, generalizes, specifies, restates and intersects). Results indicate that humans can perform this task well, with an F-score of.98 on alignment and an Fscore of.95 on semantic relations (after correction). We then describe and evaluate a combined alignment and classification algorithm, which achieves an F-score on alignment of.85 (using EuroWordNet) and an F-score of.80 on semantic relation classification. 1

We describe our participation in the Multilingual Summarization Evaluation 2005.

Abstract—This is a paper about a new resource, namely an English paraphrase dictionary extracted from the FrameNet lexicon and its example data base. I. THE LEXPAR PARAPHRASE DICTIONARY This paper describes LexPar, a lexical resource for paraphrasing English verbs. Paraphrasing within a language can be useful for various applications, such as machine translation (source-side paraphrasing can increase the likelihood of finding a good translation), multi-document summarization (paraphrasing can help find passages in different documents with the same meaning), information extraction (paraphrasing can help in detecting relevant information from seed search patterns), or dialog systems and other generation applications (paraphrasing can make the output more context-appropriate and less monotone). As a result,