TAILIEUCHUNG - Báo cáo khoa học: "Towards an Iterative Reinforcement Approach for Simultaneous Document Summarization and Keyword Extraction"

Though both document summarization and keyword extraction aim to extract concise representations from documents, these two tasks have usually been investigated independently. This paper proposes a novel iterative reinforcement approach to simultaneously extracting summary and keywords from single document under the assumption that the summary and keywords of a document can be mutually boosted. The approach can naturally make full use of the reinforcement between sentences and keywords by fusing three kinds of relationships between sentences and words, either homogeneous or heterogeneous. . | Towards an Iterative Reinforcement Approach for Simultaneous Document Summarization and Keyword Extraction Xiaojun Wan Jianwu Yang Jianguo Xiao Institute of Computer Science and Technology Peking University Beijing 100871 China wanxiaojun yangjianwu xiaojianguo @ Abstract Though both document summarization and keyword extraction aim to extract concise representations from documents these two tasks have usually been investigated independently. This paper proposes a novel iterative reinforcement approach to simultaneously extracting summary and keywords from single document under the assumption that the summary and keywords of a document can be mutually boosted. The approach can naturally make full use of the reinforcement between sentences and keywords by fusing three kinds of relationships between sentences and words either homogeneous or heterogeneous. Experimental results show the effectiveness of the proposed approach for both tasks. The corpus-based approach is validated to work almost as well as the knowledge-based approach for computing word semantics. 1 Introduction Text summarization is the process of creating a compressed version of a given document that delivers the main topic of the document. Keyword extraction is the process of extracting a few salient words or phrases from a given text and using the words to represent the text. The two tasks are similar in essence because they both aim to extract concise representations for documents. Automatic text summarization and keyword extraction have drawn much attention for a long time because they both are very important for many text applications including document retrieval document clustering etc. For example keywords of a document can be 552 used for document indexing and thus benefit to improve the performance of document retrieval and document summary can help to facilitate users to browse the search results and improve users search experience. Text summaries and keywords can be either .

• Báo cáo khoa học
• Towards an Iterative
• Reinforcement Approach for Simultaneous Document Summarization
• Keyword Extraction
• 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