TAILIEUCHUNG - Báo cáo khoa học: "Text Summarization Model based on Maximum Coverage Problem and its Variant"

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. | Text Summarization Model based on Maximum Coverage Problem and its Variant Hiroya Takamura and Manabu Okumura Precision and Intelligence Laboratory Tokyo Institute of Technology 4259 Nagatsuta Midori-ku Yokohama 226-8503 takamura@ oku@ Abstract 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-and-bound 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 Introduction Automatic text summarization is one of the tasks that have long been studied in natural language processing. This task is to create a summary or a short and concise document that describes the content of a given set of documents Mani 2001 . One well-known approach to text summarization is the extractive method which selects some linguistic units . sentences from given documents in order to generate a summary. The extractive method has an advantage that the grammaticality is guaranteed at least at the level of the linguistic units. Since the actual generation of linguistic expressions has not achieved the level of the practical use we focus on the extractive method in this paper especially the method based on the sentence extraction. Most of the extractive summarization methods rely on sequentially solving binary classification problems of determining whether each sentence should be selected or not. In such sequential methods however the viewpoint regarding whether the summary is good as a whole is not taken into consideration although a summary conveys information as a whole. We represent text .

• Báo cáo khoa học
• Text Summarization Model
• Maximum Coverage Problem
• its Variant
• scientific reports
• model language
• process natural language
• Hybrid model using bert
• Hybrid model using deep neural networks
• Rich feature for extractive text summarization
• TF IDF feature
• Large training datasets
• A Joint Model of Text
• Aspect Ratings
• Sentiment Summarization
• báo cáo khoa học
• báo cáo ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• Text Summarization Evaluation
• Lynette Hirschman
• Long Papers
• Multi Document Summarization
• Evaluative Text
• Giuseppe Carenini
• 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
• 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