Method For Multi-document Summarization Research Articles

An Empirical Study of Ontology-Based Multi-Document Summarization in Disaster Management

Domain ontology, as a conceptual model, provides a meaningful framework for semantic representation of textual information. In this paper, we explore the feasibility of using the ontology in solving multi-document summarization problems in the domain of disaster management. We provide an empirical study of different approaches in which the ontology has been used for summarization tasks. Extensive experiments on a collection of press releases relevant to Hurricane Wilma in 2005 demonstrate that ontology-based multi-document summarization methods outperform other baselines in terms of the summary quality.

Manual Typification of Source Texts and Multi-document Summaries Alignments

The Multi-document Summarization (MDS) has been focused in Natural Language Processing (NLP) and its aim is to produce automatic summaries from a collection of texts that deal with the same subject (Mani, 2001). The alignment of human-written abstracts to their source documents makes explicit the correspondences that exist in such documents/abstract pairs and create a potentially rich data source to create of rules and models to support more linguistically motivated MDS methods. In this paper we describe the typification of such alignments in the CSTNews corpus. This work is part of two larger projects called Sucinto and Sustento, and it supports MSD researches of Brazilian Portuguese language. Specifically, the typification process consisted of assigning labels to the alignment between a summary sentence and its corresponding source sentence which codify formal and content aspects of the alignment. In order to present this work, we outline the alignment, and detail the typification process, the results of our work and some conclusions.

Query Snowball: A Co-occurrence-based Approach to Multi-document Summarization for Question Answering

We propose a new method for query-oriented extractive multi-document summarization. To enrich the information need representation of a given query, we build a co-occurrence graph to obtain words that augment the original query terms. We then formulate the summarization problem as a Maximum Coverage Problem with Knapsack Constraints based on word pairs rather than single words. Our experiments with the NTCIR ACLIA question answering test collections show that our method achieves a pyramid F3-score of up to 0.313, a 36% improvement over a baseline using Maximal Marginal Relevance.

Classification of Textual E‐Mail Spam Using Data Mining Techniques

A new method for clustering of spam messages collected in bases of antispam system is offered. The genetic algorithm is developed for solving clustering problems. The objective function is a maximization of similarity between messages in clusters, which is defined by k‐nearest neighbor algorithm. Application of genetic algorithm for solving constrained problems faces the problem of constant support of chromosomes which reduces convergence process. Therefore, for acceleration of convergence of genetic algorithm, a penalty function that prevents occurrence of infeasible chromosomes at ranging of values of function of fitness is used. After classification, knowledge extraction is applied in order to get information about classes. Multidocument summarization method is used to get the information portrait of each cluster of spam messages. Classifying and parametrizing spam templates, it will be also possible to define the thematic dependence from geographical dependence (e.g., what subjects prevail in spam messages sent from certain countries). Thus, the offered system will be capable to reveal purposeful information attacks if those occur. Analyzing origins of the spam messages from collection, it is possible to define and solve the organized social networks of spammers.

Using topic themes for multi-document summarization

The problem of using topic representations for multidocument summarization (MDS) has received considerable attention recently. Several topic representations have been employed for producing informative and coherent summaries. In this article, we describe five previously known topic representations and introduce two novel representations of topics based on topic themes. We present eight different methods of generating multidocument summaries and evaluate each of these methods on a large set of topics used in past DUC workshops. Our evaluation results show a significant improvement in the quality of summaries based on topic themes over MDS methods that use other alternative topic representations.

Constructing a taxonomy to support multi-document summarization of dissertation abstracts

This paper reports part of a study to develop a method for automatic multi-document summarization. The current focus is on dissertation abstracts in the field of sociology. The summarization method uses macro-level and micro-level discourse structure to identify important information that can be extracted from dissertation abstracts, and then uses a variable-based framework to integrate and organize extracted information across dissertation abstracts. This framework focuses more on research concepts and their research relationships found in sociology dissertation abstracts and has a hierarchical structure. A taxonomy is constructed to support the summarization process in two ways: (1) helping to identify important concepts and relations expressed in the text, and (2) providing a structure for linking similar concepts in different abstracts. This paper describes the variable-based framework and the summarization process, and then reports the construction of the taxonomy for supporting the summarization process. An example is provided to show how to use the constructed taxonomy to identify important concepts and integrate the concepts extracted from different abstracts.

질의응답을 위한 복수문서 요약에 관한 실험적 연구

This experimental study proposes a multi-document summarization method that produces optimal summaries in which users can find answers to their queries. In order to identify the most effective method for this purpose, the performance of the three summarization methods were compared. The investigated methods are sentence clustering, passage extraction through spreading activation, and clustering-passage extraction hybrid methods. The effectiveness of each summarizing method was evaluated by two criteria used to measure the accuracy and the redundancy of a summary. The passage extraction method using the sequential bnb search algorithm proved to be most effective in summarizing multiple documents with regard to summarization precision. This study proposes the passage extraction method as the optimal multi-document summarization method. 攀＊＊＊ 본 연구는 연세대학교 대학원 박사학위논문의 일부를 요약한 것임.＊＊＊ 연세대학교 문헌정보학과 시간강사(shchoi@lis.yonsei.ac.kr)＊＊＊＊연세대학교 문헌정보학과 교수(ymchung@yonsei.ac.kr) 논문접수일자 : 2004년 8월 27일 게재확정일자 : 2004년 9월 13일攀攀