Multiple-document Summarization Research Articles

Comprehensive Survey of Abstractive Text Summarization Techniques

Text summarization using pre-trained encoders has become a crucial technique for efficiently managing large volumes of text data. The rise of automatic summarization systems addresses the need to process ever-increasing data while meeting user-specific requirements. Recent scientific research highlights significant advancements in abstractive summarization, with a particular focus on neural network-based methods. A detailed review of various neural network models for abstractive summarization identifies five key components essential to their design: encoder-decoder architecture, mechanisms, training strategies and optimization algorithms, dataset selection, and evaluation metrics. Each of these elements is pivotal in enhancing the summarization process. This study aims to provide a thorough understanding of the latest developments in neural network-based abstractive summarization models, offering insights into the evolving field and underscoring the associated challenges. Qualitative analysis using a concept matrix reveals common design trends in contemporary neural abstractive summarization systems. Notably, BERT-based encoder-decoder models have emerged as leading innovations, representing the most recent progress in the field. Based on the insights from this review, the study recommends integrating pre-trained language models with neural network techniques to achieve optimal performance in abstractive summarization tasks. As the volume of online information continues to surge, the field of automatic text summarization has garnered significant attention within the Natural Language Processing (NLP) community. Spanning over five decades, researchers have approached this problem from diverse angles, exploring various domains and employing a multitude of paradigms. This survey aims to delve into some of the most pertinent methodologies, focusing on both single-document and multiple-document summarization techniques, with a particular emphasis on empirical methods and extractive approaches. Additionally, the survey explores promising strategies that target specific intricacies of the summarization task. Notably, considerable attention is dedicated to the automatic evaluation of summarization systems, recognizing its pivotal role in guiding future research endeavors.

Entailment and Spectral Clustering based Single and Multiple Document Summarization

Entailment and Spectral Clustering based Single and Multiple Document Summarization

Modified LexRank for Tweet Summarization

Summary generation is an important process in those conditions where the user needs to obtain the key features of the document without having to go through the whole document itself. The summarization process is of basically two types: 1) Single document Summarization and, 2) Multiple Document Summarization. But here the microblogging environment is taken into account which have a restriction on the number of characters contained within a post. Therefore, single document summarizers are not applicable to this condition. There are many features along which the summarization of the microblog post can be done for example, post's topic, it's posting time, happening of the event, etc. This paper proposes a method that includes the temporal features of the microblog posts to develop an extractive summary of the event from each and every post, which will further increase the quality of the summary created as it includes all the key features in the summary.

WNavis: Navigating Wikipedia semantically with an SNA-based summarization technique

Link-based applications like Wikipedia are becoming increasingly popular because they provide users with an efficient way to find needed knowledge, such as searching for definitions and information about a particular topic, and exploring articles on related topics. This work introduces a semantics-based navigation application called WNavis, to facilitate information-seeking activities in internal link-based websites in Wikipedia. WNavis is based on the theories and techniques of link mining, semantic relatedness analysis and text summarization. Our goal is to develop an application that helps users find related articles for a seed query (topic) easily and then quickly check the content of articles to explore a new concept or topic in Wikipedia. Technically, we construct a preliminary topic network by analyzing the internal links of Wikipedia and applying the normalized Google distance algorithm to quantify the strength of the semantic relationships between articles via key terms. Because not all the content of articles in Wikipedia is relevant to users' information needs, it is desirable to locate specific information for users and enable them to quickly explore and read topic-related articles. Accordingly, we propose an SNA-based single and multiple-document summarization technique that can extract meaningful sentences from articles. We applied a number of intrinsic and extrinsic evaluation methods to demonstrate the efficacy of the summarization techniques in terms of precision, and recall. The results suggest that the proposed summarization technique is effective. Our findings have implications for the design of a navigation tool that can help users explore related articles in Wikipedia quickly.

A Cloud-Based Recommender System - A Case Study of Delicacy Recommendation

Delicacy recommendation services are the trend of the future. In this paper, we propose an effective decision support systems (DSS), the Cloud-Based Recommender System (CBRS), which provides the introduction and commentaries of delicacies and restaurants with relevant recommendation. CBRS provides the web content retrieval agent (WCRA) and multiple document summarization (MDS) technology to generate summary of commentaries. Finally, CBRS combines with the cloud computing for MDS to provide delicacy recommendation services.

Summary of FAQs from a topical forum based on the native composition structure

This research improves the presentation of an automatic multiple-document summarization system, which produces the frequently asked questions (FAQs) of a topical forum. The design and findings of the proposed presentation structure based on a four-part pattern of traditional Chinese articles are presented. An experiment was designed to conduct an objective experimental analysis based on criteria consisting of compression rate, recall rate, and precision rate, as well as a subjective experimental analysis based on user acceptance in terms of the indication, readability, appropriate number of sentences, and structure of the summary. The experimental results show that the proposed summary presentation structure with both domain-terminology corpus methods produced a significantly improved summary presentation compared with the original system. Nevertheless, the FAQ summary presentation system could have used complete sentences instead of partial sentences from the original articles for improved readability.

Mobile merchandise evaluation service using novel information retrieval and image recognition technology

Consumers’ purchasing behavior has obviously changed in recent years with developments in social economics. This change has been evident in the decreased ratio of planned purchases but not in the increase of planned (or spontaneous) purchases. This act of spontaneous or otherwise unplanned purchasing is called “impulse buying”. However, buying under these conditions costs more money always comes with negative responses, such as complaints and regret. Therefore, we propose and have designed a new merchandise recommendation system, the Mobile Merchandise Evaluation Service Platform (MMESP). This is a three-tier system composed of Real-time Merchandise Identifying System (RMIS), Real-time Merchandise Evaluation System (RMES), and Real-time Merchandise Recommendation System (RMRS). With this system, Mobile Users (MUs) take pictures of merchandise and send them to MMESP, RMIS integrates Region Adjacency Graph (RAG) and Self-Organizing Maps (SOM) to gather information on the merchandise through those photographs, and. RMES and RMRS provide Intelligence Agents (IAs) and Multiple Document Summarization (MDS) to summarize recommendations on merchandise for MUs, all in real time.