TAILIEUCHUNG - Báo cáo khoa học: "Graph-based Ranking Algorithms for Sentence Extraction, Applied to Text Summarization"

This paper presents an innovative unsupervised method for automatic sentence extraction using graphbased ranking algorithms. We evaluate the method in the context of a text summarization task, and show that the results obtained compare favorably with previously published results on established benchmarks. | Graph-based Ranking Algorithms for Sentence Extraction Applied to Text Summarization Rada Mihalcea Department of Computer Science University of North Texas rada@ Abstract This paper presents an innovative unsupervised method for automatic sentence extraction using graphbased ranking algorithms. We evaluate the method in the context of a text summarization task and show that the results obtained compare favorably with previously published results on established benchmarks. 1 Introduction Graph-based ranking algorithms such as Klein-berg s HITS algorithm Kleinberg 1999 or Google s PageRank Brin and Page 1998 have been traditionally and successfully used in citation analysis social networks and the analysis of the link-structure of the World Wide Web. In short a graph-based ranking algorithm is a way of deciding on the importance of a vertex within a graph by taking into account global information recursively computed from the entire graph rather than relying only on local vertex-specific information. A similar line of thinking can be applied to lexical or semantic graphs extracted from natural language documents resulting in a graph-based ranking model called TextRank Mihalcea and Tarau 2004 which can be used for a variety of natural language processing applications where knowledge drawn from an entire text is used in making local ranking selection decisions. Such text-oriented ranking methods can be applied to tasks ranging from automated extraction of keyphrases to extractive summarization and word sense disambiguation Mihalcea et al. 2004 . In this paper we investigate a range of graphbased ranking algorithms and evaluate their application to automatic unsupervised sentence extraction in the context of a text summarization task. We show that the results obtained with this new unsupervised method are competitive with previously developed state-of-the-art systems. 2 Graph-Based Ranking Algorithms Graph-based ranking algorithms are essentially a way of .

• Báo cáo khoa học
• Graph based Ranking Algorithms
• Sentence Extraction
• Applied to Text Summarization
• báo cáo khoa học
• mô hình ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• Bài giảng Xử lý ngôn ngữ tự nhiên
• Ngôn ngữ tự nhiên
• Kỹ thuật lập trình
• Xử lý nhập nhằng ngữ nghĩa
• Xử lý ngữ nghĩa
• Xử lý ngôn ngữ
• Natural Language Processing
• Ngôn ngữ lập trình
• Nhập nhằng cấu trúc
• Tri thức về ngôn ngữ
• Mô hình n gram
• Dịch máy
• Phương pháp dịch máy
• Hiểu ngôn ngữ
• Phân tích ngữ nghĩa
• Phân loại tin tự động
• Biểu diễn vị từ
• Thuộc tính về sự kiện
• Thuộc tính về sự kiện
• Mô hình ngôn ngữ Google Book N grams
• Mô hình ngôn ngữ KenLM
• Phương pháp làm mịn
• Tách từ tiếng Việt
• Gán nhãn từ loại
• Phân tích cú pháp
• Nghĩa từ vựng
• Phân giải nhập nhằng từ
• Phân loại văn bản
• Lexical chain
• Phản hồi thông tin
• Bài toán PTCP
• Cấu trúc ngữ pháp
• Phân tích cú pháp xác suất
• CKY kết hợp xác suất
• Văn phạm phi ngữ cảnh xác suất
• Hình thái học