Multi-document summarization

About: Multi-document summarization is a research topic. Over the lifetime, 2270 publications have been published within this topic receiving 71850 citations.

Automatic summarization for popular songs

Abstract: In this paper, we present an automatic summarization approach for popular songs. The approach includes two stages. First, a rough summary of the song is extracted efficiently by the energy information and the songpsilas structure, and then the segment boundaries of the summary are detected. Different from most previous works, the summary has a more complete meaning and a varied length instead of a fixed length, therefore it is much helpful for popular songpsilas audition and management. The algorithm is tested and evaluated by objective means. Experimental results show that the proposed approach achieves satisfactory results.

Text Summarization Using Demographic Algorithm with Clustering Method

Abstract: Text summarization is a data mining process of extracting the summary or zest from one or more documents. A summary is nothing but the actual theme of the document or set of documents. Most commonly document summery is considered to be the sentences or words from set of documents or a single document that appear more number of times in the document with corresponding to the other words. But a report on solar power may emphasis on several aspects of solar energy and may not actually have the term solar power repeated many a times. Therefore sophisticated algorithms are needed to extract the summary from the documents. There have been several algorithms on Text and Document summarizations, utilization various aspects of similarity measures, clustering, lexical rules and distance measures. It is understood from the literature that no single technique can give best interpretation or desired result in the summarization process. Therefore in this work we propose a summarization technique with document clustering and to improve its efficiency we will include demographic algorithm in this technique.

An enhanced LSA-based approach for update summarization

Abstract: Update summarization is a challenge in automatic text summarization. The task aims to distill evolved messages from a collection of new articles, under the assumption that the reader has already browsed the previous articles. In this paper, we reviewed some state-of-the-art approaches for extracting update summarization and then focused on a LSA-based one. After the analysis of LSA-based approach's framework, we improved the approach by enhancing the approach's performance in accuracy. First, we utilized TOPIC SIGNATURE algorithm to extract the terms' novel information and incorporated the information to the process of evaluating topic's novelty score, which makes the evaluation more accuracy. Second, we excluded the least novel and important topics when generating summary, which helps improving the quality of the summary. The evaluation result on the update summarization task of Text Analysis Conference (TAC) 2008 indicates the validity of our modification.

Text Summarization as a Multi-objective Optimization Task: Applying Harmony Search to Extractive Multi-Document Summarization

Abstract: Today, automated extractive text summarization is one of the most common techniques for organizing information. In extractive summarization, the most appropriate sentences are selected from the text and build a representative summary. Therefore, probing for the best sentences is a fundamental task.This paper has coped with extractive summarization as a multi-objective optimization problem and proposed a language-independent, semantic-aware approach that applies the harmony search algorithm to generate appropriate multi-document summaries. It learns the objective function from an extra set of reference summaries and then generates the best summaries according to the trained function. The system also performs some supplementary activities for better achievements. It expands the sentences by using an inventive approach that aims at tuning conceptual densities in the sentences towards important topics. Furthermore, we introduced an innovative clustering method for identifying important topics and reducing redundancies. A sentence placement policy based on the Hamiltonian shortest path was introduced for producing readable summaries.The experiments were conducted on DUC2002, DUC2006 and DUC2007 datasets. Experimental results showed that the proposed framework could assist the summarization process and yield better performance. Also, it was able to generally outperform other cited summarizer systems.

New Formats and Interfaces for Multi-Document News Summarization and its Evaluation

Abstract: News production, delivery, and consumption are increasing in ubiquity and speed, spreading over more software and hardware platforms, in particular mobile devices. This has led to an increasing interest in automated methods for multi-document summarization. We start this chapter with discussing several new alternatives for automated news summarization, with a particular focus on temporal text mining, graphbased methods, and graphical interfaces. Then we present automated and user-centric frameworks for cross-evaluating summarization methods that output different summary formats, and describe the challenges associated with each evaluation framework. Based on the results of our user studies, we argue that it is crucial for effective summarization to integrate the user into sense-making through usable, entertaining and ultimately useful interactive summarization-plus-document-search interfaces. In particular, graph-based methods and interfaces may be a better preparation for people to concentrate on what is essential in a collection of texts, and thus may be a key to enhancing the summary evaluation process by replacing the “one gold standard fits all” approach with carefully designed user studies built upon a variety of summary representation formats.