Personalized Text Content Summarizer for Mobile Learning: An Automatic Text Summarization System with Relevance Based Language Model

2012 ◽  
Author(s):  
Guangbing Yang ◽  
Dunwei Wen ◽  
Kinshuk ◽  
Nian-Shing Chen ◽  
Erkki Sutinen
Keyword(s):  
Mobile Learning ◽  
Language Model ◽  
Text Summarization ◽  
Automatic Text Summarization ◽  
Summarization System ◽  
Text Content ◽  
Automatic Text

scholarly journals Automatic Text Summarization of COVID-19 Research Articles Using Recurrent Neural Networks and Coreference Resolution

2021 ◽  
Author(s):  
Mahsa Afsharizadeh ◽  
Hossein Ebrahimpour-Komleh ◽  
Ayoub Bagheri
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Text Summarization ◽  
Medical Community ◽  
Research Articles ◽  
Coreference Resolution ◽  
Extractive Summarization ◽  
Automatic Text Summarization ◽  
Summarization System ◽  
Automatic Text

Purpose: Pandemic COVID-19 has created an emergency for the medical community. Researchers require extensive study of scientific literature in order to discover drugs and vaccines. In this situation where every minute is valuable to save the lives of hundreds of people, a quick understanding of scientific articles will help the medical community. Automatic text summarization makes this possible. Materials and Methods: In this study, a recurrent neural network-based extractive summarization is proposed. The extractive method identifies the informative parts of the text. Recurrent neural network is very powerful for analyzing sequences such as text. The proposed method has three phases: sentence encoding, sentence ranking, and summary generation. To improve the performance of the summarization system, a coreference resolution procedure is used. Coreference resolution identifies the mentions in the text that refer to the same entity in the real world. This procedure helps to summarization process by discovering the central subject of the text. Results: The proposed method is evaluated on the COVID-19 research articles extracted from the CORD-19 dataset. The results show that the combination of using recurrent neural network and coreference resolution embedding vectors improves the performance of the summarization system. The Proposed method by achieving the value of ROUGE1-recall 0.53 demonstrates the improvement of summarization performance by using coreference resolution embedding vectors in the RNN-based summarization system. Conclusion: In this study, coreference information is stored in the form of coreference embedding vectors. Jointly use of recurrent neural network and coreference resolution results in an efficient summarization system.

Implementasi Algoritma Graf dan Algoritma Genetika pada Peringkasan Single Document

Repositor ◽  
2020 ◽  
Vol 2 (11) ◽  
pp. 1521
Author(s):  
Lina Dwi Yulianti ◽  
Setio Basuki ◽  
Yufis Azhar
Keyword(s):  
Genetic Algorithms ◽  
Graph Algorithms ◽  
System Development ◽  
High Accuracy ◽  
Text Summarization ◽  
Test Results ◽  
The Core ◽  
Automatic Text Summarization ◽  
Summarization System ◽  
Automatic Text

In today's technological advancements, finding information is easier and faster. But not a little information that is not true or commonly referred to as hoaxes. Therefore, information must be obtained from several sources to ensure the accuracy of the information. Automatic Text Summarization System is a system used for text based document summarization. This system can help find the core of a news document, so it does not require much time to read. Researchers use Graph Algorithms and Genetic Algorithms in system development. From the test results obtained by the accuracy of the system produced by the system with manual numbers have a cosine similarity value of 71.21%. This can prove that the system built can be used by users because the results of tests carried out get high accuracy values.

The effectiveness of automatic text summarization in mobile learning contexts

2013 ◽  
Vol 68 ◽  
pp. 233-243 ◽  
Author(s):  
Guangbing Yang ◽  
Nian-Shing Chen ◽  
Kinshuk ◽  
Erkki Sutinen ◽  
Terry Anderson ◽  
...  
Keyword(s):  
Mobile Learning ◽  
Text Summarization ◽  
Automatic Text Summarization ◽  
Learning Contexts ◽  
Automatic Text

Techniques and Issues in Text Mining

2020 ◽  
Vol 17 (9) ◽  
pp. 4368-4374
Author(s):  
Perpetua F. Noronha ◽  
Madhu Bhan
Keyword(s):  
Language Processing ◽  
Text Summarization ◽  
Digital Data ◽  
Text Documents ◽  
Significant Information ◽  
Digital Era ◽  
Automatic Text Summarization ◽  
Text Content ◽  
Available Information ◽  
Automatic Text

Digital data in huge amount is being persistently generated at an unparalleled and exponential rate. In this digital era where internet stands the prime source for generating incredible information, it is vital to develop better means to mine the available information rapidly and most capably. Manual extraction of the salient information from the large input text documents is a time consuming and inefficient task. In this fast-moving world, it is difficult to read all the text-content and derive insights from it. Automatic methods are required. The task of probing for relevant documents from the large number of sources available, and consuming apt information from it is a challenging task and is need of the hour. Automatic text summarization technique can be used to generate relevant and quality information in less time. Text Summarization is used to condense the source text into a brief summary maintaining its salient information and readability. Generating summaries automatically is in great demand to attend to the growing and increasing amount of text data that is obtainable online in order to mark out the significant information and to consume it faster. Text summarization is becoming extremely popular with the advancement in Natural Language Processing (NLP) and deep learning methods. The most important gain of automatic text summarization is, it reduces the analysis time. In this paper we focus on key approaches to automatic text summarization and also about their efficiency and limitations.

scholarly journals MultiSumm: Towards a Unified Model for Multi-Lingual Abstractive Summarization

2020 ◽  
Vol 34 (01) ◽  
pp. 11-18
Author(s):  
Yue Cao ◽  
Xiaojun Wan ◽  
Jinge Yao ◽  
Dian Yu
Keyword(s):  
Language Model ◽  
Text Summarization ◽  
Additional Contribution ◽  
Automatic Text Summarization ◽  
Proposed Model ◽  
Back Translation ◽  
Model Training ◽  
Abstractive Summarization ◽  
Multiple Languages ◽  
Automatic Text

Automatic text summarization aims at producing a shorter version of the input text that conveys the most important information. However, multi-lingual text summarization, where the goal is to process texts in multiple languages and output summaries in the corresponding languages with a single model, has been rarely studied. In this paper, we present MultiSumm, a novel multi-lingual model for abstractive summarization. The MultiSumm model uses the following training regime: (I) multi-lingual learning that contains language model training, auto-encoder training, translation and back-translation training, and (II) joint summary generation training. We conduct experiments on summarization datasets for five rich-resource languages: English, Chinese, French, Spanish, and German, as well as two low-resource languages: Bosnian and Croatian. Experimental results show that our proposed model significantly outperforms a multi-lingual baseline model. Specifically, our model achieves comparable or even better performance than models trained separately on each language. As an additional contribution, we construct the first summarization dataset for Bosnian and Croatian, containing 177,406 and 204,748 samples, respectively.

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