Shiyan Ou

Design and development of a concept-based multi-document summarization system for research abstracts

This paper describes a new concept-based multi-document summarization system that employs discourse parsing, information extraction and information integration. Dissertation abstracts in the field of sociology were selected as sample documents for this study. The summarization process includes four major steps — (1) parsing dissertation abstracts into five standard sections; (2) extracting research concepts (often operationalized as research variables) and their relationships, the research methods used and the contextual relations from specific sections of the text; (3) integrating similar concepts and relationships across different abstracts; and (4) combining and organizing the different kinds of information using a variable-based framework, and presenting them in an interactive web-based interface. The accuracy of each summarization step was evaluated by comparing the system-generated output against human coding. The user evaluation carried out in the study indicated that the majority of subjects (70%) preferred the concept-based summaries generated using the system to the sentence-based summaries generated using traditional sentence extraction techniques.

A Hierarchical Framework for Multi-document Summarization of Dissertation Abstracts

This paper reports initial work on developing methods for automatic generation of multi-document summaries of dissertation abstracts in a digital library. The focus is on automatically generating a summary of a set of dissertation abstracts retrieved in response to user query, and presenting the summary using a visualization method. A hierarchical variable-based framework for multi-document summarization of dissertation abstracts in sociology and psychology is presented. The framework makes use of macro-level and microlevel discourse structure of dissertation abstracts as well as cross-document structure. The micro-level structure of problem statements found in a sample of 50 dissertation abstracts was analyzed, and the common features found are described in the paper. A demonstration prototype with a tree-view interface for presenting multi-document abstracts has been implemented.

A multi-document summarization system for sociology dissertation abstracts: design, implementation and evaluation

The design, implementation and evaluation of a multi-document summarization system for sociology dissertation abstracts are described. The system focuses on extracting variables and their relationships from different documents, integrating the extracted information, and presenting the integrated information using a variable-based framework. Two important summarization steps – information extraction and information integration were evaluated by comparing system-generated output against human-generated output. Results indicate that the system-generated output achieves good precision and recall while extracting important concepts from each document, as well as good clusters of similar concepts from the set of documents.

Multi‐document summarization of dissertation abstracts using a variable‐based framework

This paper reports initial work on developing a method for automatic construction of multidocument summaries of sets of domain-specific dissertation abstracts. A variable-based framework for multi-document summarization of dissertation abstracts in the field of sociology and psychology that makes use of the macro-level and micro-level discourse structure of dissertation

Development and evaluation of a multi-document summarization method focusing on research concepts and their research relationships

This paper reports the design and evaluation of a method for summarizing a set of related research abstracts. This summarization method extracts research concepts and their research relationships from different abstracts, integrates the extracted information across abstracts, and presents the integrated information in a Web-based interface to generate a multi-document summary. This study focused on sociology dissertation abstracts, but can be extended to other research abstracts. The summarization method was evaluated in a user study to assess the quality and usefulness of the generated summaries in comparison to a sentence extraction method used in MEAD and a method that extracts only research objective sentences. The evaluation results indicated that the majority of sociology researchers preferred our variable-based summary generated with the use of a taxonomy.

Multi‐document summarization of news articles using an event‐based framework

Purpose – The purpose of this research is to develop a method for automatic construction of multi‐document summaries of sets of news articles that might be retrieved by a web search engine in response to a user query.Design/methodology/approach – Based on the cross‐document discourse analysis, an event‐based framework is proposed for integrating and organizing information extracted from different news articles. It has a hierarchical structure in which the summarized information is presented at the top level and more detailed information given at the lower levels. A tree‐view interface was implemented for displaying a multi‐document summary based on the framework. A preliminary user evaluation was performed by comparing the framework‐based summaries against the sentence‐based summaries.Findings – In a small evaluation, all the human subjects preferred the framework‐based summaries to the sentence‐based summaries. It indicates that the event‐based framework is an effective way to summarize a set of news art...