TAILIEUCHUNG - Báo cáo khoa học: "Large-Margin Learning of Submodular Summarization Models"

In this paper, we present a supervised learning approach to training submodular scoring functions for extractive multidocument summarization. By taking a structured prediction approach, we provide a large-margin method that directly optimizes a convex relaxation of the desired performance measure. | Large-Margin Learning of Submodular Summarization Models Ruben Sipos Dept. of Computer Science Cornell University Ithaca NY 14853 USA Pannaga Shivaswamy Dept. of Computer Science Cornell University Ithaca NY 14853 USA Thorsten Joachims Dept. of Computer Science Cornell University Ithaca NY 14853 USA rs@ pannaga@ tj@ Abstract In this paper we present a supervised learning approach to training submodu-lar scoring functions for extractive multidocument summarization. By taking a structured prediction approach we provide a large-margin method that directly optimizes a convex relaxation of the desired performance measure. The learning method applies to all submodular summarization methods and we demonstrate its effectiveness for both pairwise as well as coverage-based scoring functions on multiple datasets. Compared to state-of-the-art functions that were tuned manually our method significantly improves performance and enables high-fidelity models with number of parameters well beyond what could reasonably be tuned by hand. 1 Introduction Automatic document summarization is the problem of constructing a short text describing the main points in a set of document s . Example applications range from generating short summaries of news articles to presenting snippets for URLs in web-search. In this paper we focus on extractive multi-document summarization where the final summary is a subset of the sentences from multiple input documents. In this way extractive summarization avoids the hard problem of generating well-formed natural-language sentences since only existing sentences from the input documents are presented as part of the summary. A current state-of-the-art method for document summarization was recently proposed by Lin and Bilmes 2010 using a submodular scoring function based on inter-sentence similarity. On the one hand this scoring function rewards summaries that are similar to many sentences in the original documents .

• Báo cáo khoa học
• Large Margin Learning
• Submodular Summarization Models
• Thorsten Joachims
• scientific reports
• model language
• process natural language
• Dialog Control
• Natural Language System
• Dialog Control in a Natural Language System
• Expanding the Horizons
• Natural Language Interfaces
• Phil Hayes
• PROSPECTS OR PRACTICAL
• NATURAL LANGUAGE SYSTEMS
• Workshops
• NATURAL LANGUAGE
• INTERACTION WITH MACHINES
• Long Papers
• COMPUTER INTEBFACE DESIGN MURRAY TUROFF DEPARTMENT OF COMPUTER
• IiVFORMATION SCIENCE IIEW JERSEY INSTITUTE OF TECHNOLOGY
• EVALUATION OF NATURAL LANGUAGE
• INTERFACES TO DATABASE SYSTEMS
• A PANEL DISCUSSION
• database query system
• J
• Norwood Crout
• INTERFACES TO DATA BASE SYSTEMS
• Bozena Henisz
• INTERPRETING NATURAL LANGUAGE
• DATABASE UPDATES
• S
• Jermld Kaplan Jim David
• TRANSPORTABLE NATURAL LANGUAGE
• INTERFACES TO DATABASES
• Gary G
• Hendrlx and William H
• Lewis
• DATABASES THEORETICAL
• TECHNICAL ISSUES
• TRANSPORTABLE NATURAL LANGUAGE INTERFACES
• PROBLEMS AND TECHNIQUES
• Barbara J
• Grosz
• THEORETICAL TECHNICAL ISSUES
• NATURAL LANGUAGE ACCESS TO DATABASES
• R
• Petrick
• Domain Independent
• Database Access Systems
• ISSUES IN NATURAL LANGUAGE
• ACCESS TO DATABASES
• A LOGIC PROGRAMMING PERSPECTIVE
• Sharon C
• Salveter David Maier
• PLANNING NATURAL LANGUAGE
• EXPRESSIONS REFERRING
• Douglas E
• Appelt
• THE TEXT SYSTEM
• NATURAL LANGUAGE GENERATION
• Kathleen R
• M
• MENTING A DATABASE
• KNOWLEDGE REPRESENTATION
• THE KEY TO THE SELECTION PROBLEM
• E
• Jeffrey Conklin David D
• McDonald
• A KNOWLEDGE ENGINEERING
• NATURAL LANGUAGE UNDERSTANDING
• Stuart C
• Shapiro
• NATURAL LANGUAGE TEXTS
• NECESSARILY GRAMMATICAL
• EVEN COMPLETE
• UNGRAMHATICALITY
• EXTRA GRAMMATICALITY
• NATURAL LANGUAGE UNDERSTANDING SYSTEMS
• Taxonomy
• Descriptions
• Individuals in Natural
• Language Understanding
• The Use of Ooject Special Knowledge
• Natural Language Processing
• Mark H
• Bursteln