TAILIEUCHUNG - Báo cáo khoa học: "Extractive Summarization using Inter- and Intra- Event Relevance"

Event-based summarization attempts to select and organize the sentences in a summary with respect to the events or the sub-events that the sentences describe. Each event has its own internal structure, and meanwhile often relates to other events semantically, temporally, spatially, causally or conditionally. In this paper, we define an event as one or more event terms along with the named entities associated, and present a novel approach to derive intra- and inter- event relevance using the information of internal association, semantic relatedness, distributional similarity and named entity clustering. . | Extractive Summarization using Inter- and Intra- Event Relevance Wenjie Li Mingli Wu and Qin Lu Department of Computing The Hong Kong Polytechnic University cswjli csmlwu csluqin @comp . Wei Xu and Chunfa Yuan Department of Computer Science and Technology Tsinghua University vivian00 cfyuan @ Abstract Event-based summarization attempts to select and organize the sentences in a summary with respect to the events or the sub-events that the sentences describe. Each event has its own internal structure and meanwhile often relates to other events semantically temporally spatially causally or conditionally. In this paper we define an event as one or more event terms along with the named entities associated and present a novel approach to derive intra- and inter- event relevance using the information of internal association semantic relatedness distributional similarity and named entity clustering. We then apply PageRank ranking algorithm to estimate the significance of an event for inclusion in a summary from the event relevance derived. Experiments on the DUC 2001 test data shows that the relevance of the named entities involved in events achieves better result when their relevance is derived from the event terms they associate. It also reveals that the topic-specific relevance from documents themselves outperforms the semantic relevance from a general purpose knowledge base like Word-Net. 1. Introduction Extractive summarization selects sentences which contain the most salient concepts in documents. Two important issues with it are how the concepts are defined and what criteria should be used to judge the salience of the concepts. Existing work has typically been based on techniques that extract key textual elements such as keywords also known as significant terms as weighed by their tf idf score or concepts such as events or entities with linguistic and or statistical analysis. Then sentences are selected according to either the .

• Báo cáo khoa học
• Extractive Summarization
• Inter and Intra Event Relevance
• Wenjie Li
• Mingli Wu and Qin Lu
• báo cáo khoa học
• mô hình ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• Text summarization
• Graph based methods
• Machine learning
• Summarization as a graph based problem
• A Risk Minimization Framework
• Extractive Speech Summarization
• Shih Hsiang Lin and Berlin Chen
• báo cáo ngôn ngữ
• ngôn ngữ tự nhiên
• Discovery of Topically Coherent Sentences
• Asli Celikyilmaz
• Answer Extraction
• Semantic Clustering
• Clinical Question Answering
• Event Term Clustering
• Maofu Liu
• Language Independent
• Rada Mihalcea
• Company Oriented
• Financial News
• 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
• Bài giảng Xử lý 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