A Review On Automatic Text Summarization Approaches

It has been more than 50 years since the initial investigation on automatic text summarization was started.Various techniques have been successfully used to extract the important contents from text document to represent document summary.In this study,we review some of the studies that have been conducted in this still-developing research area.It covers the basics of text summarization,the types of summarization,the methods that have been used and some areas in which text summarization has been applied.Furthermore,this paper also reviews the significant efforts which have been put in studies concerning sentence extraction,domain specific summarization and multi document summarization and provides the theoretical explanation and the fundamental concepts related to it.In addition,the advantages and limitations concerning the approaches commonly used for text summarization are also highlighted in this study

International Conference on Knowledge Management

Text summary has become a vital and more popular domain to preserve and highlight the core purpose of textual information as the amount of online information and resource texts has grown. Text summarization is the task of extracting key information from a text document. Text summarizing research in Afaan Oromoo is still rare and hasn't been thoroughly assessed. This study's primary goal was to evaluate the performance and method of extractive models on automatic extractive text summarization for Afaan Oromoo. Automatic Text summarization approaches can be classified as extractive or abstractive. Automatic abstractive text summarization was not included in this study. Existing automatic extractive text summarizing algorithms take key sentences from the source manuscript and provide a summary without changing the data. This paper examined and assessed some studies on the Afaan Oromoo Language Text Summarization system with a focus on methods and performance. In addition, the automatic extractive text summarization domain's challenges in Afaan Oromoo’s were also discussed. This paper used a systematic literature review method to examine the most recent literature in automatic extractive text summarization as it relates to the Afaan Oromoo language. We used a search engine, Google Scholar, ResearchGate, CiteseerX, peer-reviewed papers, and Academia to gather papers. After the papers were selected, the performance and automatic text summarization methods were evaluated

A literature review of abstractive summarization methods

The paper contains a literature review for automatic abstractive text summarization. The classification of abstractive text summarization methods was considered. Since the emergence of text summarization in the 1950s, techniques for summaries generation were constantly improving, but because the abstractive summarization require extensive language processing, the greatest progress was achieved only recently. Due to the current fast pace of development of both Natural Language Processing in general and Text Summarization in particular, it is essential to analyze the progress in these areas. The paper aims to give a general perspective on both the state-of-the-art and older approaches, while explaining the methods and approaches. Additionally, evaluation results of the research papers are presented

Text Summarization Techniques: A Brief Survey

In recent years, there has been a explosion in the amount of text data from a variety of sources. This volume of text is an invaluable source of information and knowledge which needs to be effectively summarized to be useful. In this review, the main approaches to automatic text summarization are described. We review the different processes for summarization and describe the effectiveness and shortcomings of the different methods.Comment: Some of references format have update

Ocular-based automatic summarization of documents: is re-reading informative about the importance of a sentence?

Automatic document summarization (ADS) has been introduced as a viable solution for reducing the time and the effort needed to read the ever-increasing textual content that is disseminated. However, a successful universal ADS algorithm has not yet been developed. Also, despite progress in the field, many ADS techniques do not take into account the needs of different readers, providing a summary without internal consistency and the consequent need to re-read the original document. The present study was aimed at investigating the usefulness of using eye tracking for increasing the quality of ADS. The general idea was of that of finding ocular behavioural indicators that could be easily implemented in ADS algorithms. For instance, the time spent in re-reading a sentence might reflect the relative importance of that sentence, thus providing a hint for the selection of text contributing to the summary. We have tested this hypothesis by comparing metrics based on the analysis of eye movements of 30 readers with the highlights they made afterward. Results showed that the time spent reading a sentence was not significantly related to its subjective value, thus frustrating our attempt. Results also showed that the length of a sentence is an unavoidable confounding because longer sentences have both the highest probability of containing units of text judged as important, and receive more fixations and re-fixations