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Integrating rhetorical-semantic relation models for query-focused summarization
- In Proceedings of the Document Understanding Conference, DUC-2006
, 2006
"... We present our recent work on query-focused summarization, focusing on our efforts in building and applying models of rhetorical-semantic relations (RSRs) such as contrast and causality. We overview ongoing work in extracting and evaluating RSR models. We describe our system for query-focused summar ..."
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Cited by 10 (1 self)
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We present our recent work on query-focused summarization, focusing on our efforts in building and applying models of rhetorical-semantic relations (RSRs) such as contrast and causality. We overview ongoing work in extracting and evaluating RSR models. We describe our system for query-focused summarization, focusing on an enhanced, feature-based framework. We present results of experiments to measure the impact of both RSR and other features on selection and ordering of summary content. We conclude with an overview of results from the official DUC06 evaluation. 1
Long-Answer Question Answering and Rhetorical-Semantic Relations
, 2007
"... Over the past decade, Question Answering (QA) has generated considerable interest and participation in the fields of Natural Language Processing and Information Retrieval. Conferences such as TREC, CLEF and DUC have examined various aspects of the QA task in the academic community. In the commercial ..."
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Cited by 4 (0 self)
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Over the past decade, Question Answering (QA) has generated considerable interest and participation in the fields of Natural Language Processing and Information Retrieval. Conferences such as TREC, CLEF and DUC have examined various aspects of the QA task in the academic community. In the commercial world, major search engines from Google, Microsoft and Yahoo have integrated basic QA capabilities into their core web search. These efforts have focused largely on so-called “factoid ” questions seeking a single fact, such as the birthdate of an individual or the capital city of a country. Yet in the past few years, there has been growing recognition of a broad class of “long-answer ” questions which cannot be satisfactorily answered in this framework, such as those seeking a definition, explanation, or other descriptive information in response. In this thesis, we consider the problem of answering such questions, with particular focus on the contribution to be made by integrating rhetorical and semantic models. We present DefScriber, a system for answering definitional (“What is X?”), biographical (“Who is X?”) and other long-answer questions using a hybrid of goal- and data-driven methods. Our goal-driven, or top-down, approach is motivated by a set of definitional pred-
Utterances Assessment in Chat Conversations
"... Abstract. With the continuous evolution of collaborative environments, the needs of automatic analyses and assessment of participants in instant messenger conferences (chat) have become essential. For these aims, on one hand, a series of factors based on natural language processing (including lexica ..."
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Cited by 3 (3 self)
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Abstract. With the continuous evolution of collaborative environments, the needs of automatic analyses and assessment of participants in instant messenger conferences (chat) have become essential. For these aims, on one hand, a series of factors based on natural language processing (including lexical analysis and Latent Semantic Analysis) and data-mining have been taken into consideration. On the other hand, in order to thoroughly assess participants, measures as Page’s essay grading, readability and social networks analysis metrics were computed. The weights of each factor in the overall grading system are optimized using a genetic algorithm whose entries are provided by a perceptron in order to ensure numerical stability. A gold standard has been used for evaluating the system’s performance.
A Reflection of the Whole Picture Is Not Always What You Want, But That Is What We Give You
"... We evaluate a novel method for automatic text summarization through text extraction. It attempts to find the summary most similar to the original text, thus giving an overview of all the contents. It also evaluates whole summaries, making no judgments on for instance individual sentences. A greedy s ..."
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Cited by 1 (0 self)
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We evaluate a novel method for automatic text summarization through text extraction. It attempts to find the summary most similar to the original text, thus giving an overview of all the contents. It also evaluates whole summaries, making no judgments on for instance individual sentences. A greedy search strategy is used to search through the space of possible summaries and select the best summary of those found. When evaluated on English abstracts from the Document Understanding Conferences our method performed fairly well. In this paper we evaluate it on Swedish human produced extracts. It performs poorly, which was expected since these extracts were not produced to reflect the whole contents of the texts. They only cover the most important topic.
A Reflection of the Whole Picture Is Not Always What You Want, But That Is What We Give You
"... We evaluate a novel method for automatic text summarization through text extraction. It attempts to find the summary most similar to the original text, thus giving an overview of all the contents. It also evaluates whole summaries, making no judgments on for instance individual sentences. A greedy s ..."
Abstract
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We evaluate a novel method for automatic text summarization through text extraction. It attempts to find the summary most similar to the original text, thus giving an overview of all the contents. It also evaluates whole summaries, making no judgments on for instance individual sentences. A greedy search strategy is used to search through the space of possible summaries and select the best summary of those found. When evaluated on English abstracts from the Document Understanding Conferences our method performed fairly well. In this paper we evaluate it on Swedish human produced extracts. It performs poorly, which was expected since these extracts were not produced to reflect the whole contents of the texts. They only cover the most important topic.
Abstract Integrating Rhetorical-Semantic Relation Models
"... We present our recent work on query-focused summarization, focusing on our efforts in building and applying models of rhetorical-semantic relations (RSRs) such as contrast and causality. We overview ongoing work in extracting and evaluating RSR models. We describe our system for query-focused summar ..."
Abstract
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We present our recent work on query-focused summarization, focusing on our efforts in building and applying models of rhetorical-semantic relations (RSRs) such as contrast and causality. We overview ongoing work in extracting and evaluating RSR models. We describe our system for query-focused summarization, focusing on an enhanced, feature-based framework. We present results of experiments to measure the impact of both RSR and other features on selection and ordering of summary content. We conclude with an overview of results from the official DUC06 evaluation. 1
