TAILIEUCHUNG - Báo cáo khoa học: "A Bottom-up Approach to Sentence Ordering for Multi-document Summarization"

Ordering information is a difficult but important task for applications generating natural-language text. We present a bottom-up approach to arranging sentences extracted for multi-document summarization. To capture the association and order of two textual segments (eg, sentences), we define four criteria, chronology, topical-closeness, precedence, and succession. These criteria are integrated into a criterion by a supervised learning approach. We repeatedly concatenate two textual segments into one segment based on the criterion until we obtain the overall segment with all sentences arranged. . | A Bottom-up Approach to Sentence Ordering for Multi-document Summarization Danushka Bollegala Naoaki Okazaki Mitsuru Ishizuka Graduate School of Information Science and Technology The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan danushka okazaki @ ishizuka@ Abstract Ordering information is a difficult but important task for applications generating natural-language text. We present a bottom-up approach to arranging sentences extracted for multi-document summarization. To capture the association and order of two textual segments eg sentences we define four criteria chronology topical-closeness precedence and succession. These criteria are integrated into a criterion by a supervised learning approach. We repeatedly concatenate two textual segments into one segment based on the criterion until we obtain the overall segment with all sentences arranged. Our experimental results show a significant improvement over existing sentence ordering strategies. 1 Introduction Multi-document summarization MDS Radev and McKeown 1999 tackles the information overload problem by providing a condensed version of a set of documents. Among a number of sub-tasks involved in MDS eg sentence extraction topic detection sentence ordering information extraction sentence generation etc. most MDS systems have been based on an extraction method which identifies important textual segments eg sentences or paragraphs in source documents. It is important for such MDS systems to determine a coherent arrangement of the textual segments extracted from multi-documents in order to reconstruct the text structure for summarization. Ordering information is also essential for Research Fellow of the Japan Society for the Promotion of Science JSPS other text-generation applications such as Question Answering. A summary with improperly ordered sentences confuses the reader and degrades the qual-ity reliability of the summary itself. Barzi-lay 2002 has provided

• Báo cáo khoa học
• A Bottom up Approach to Sentence Ordering
• Multi document Summarization
• Danushka Bollegala Naoaki Okazaki
• 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
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• Hình thái học