scholarly journals A Pointer Generator Network Model to Automatic Text Summarization and Headline Generation

2019 ◽  
Vol 8 (5S3) ◽  
pp. 447-451
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Recurrent Neural Network ◽  
Text Summarization ◽  
Daily Mail ◽  
Automatic Text Summarization ◽  
Generator Model ◽  
Abstractive Summarization ◽  
Automatic Text

In a world where information is growing rapidly every single day, we need tools to generate summary and headlines from text which is accurate as well as short and precise. In this paper, we have described a method for generating headlines from article. This is done by using hybrid pointer-generator network with attention distribution and coverage mechanism on article which generates abstractive summarization followed by the application of encoder-decoder recurrent neural network with LSTM unit to generate headlines from the summary. Hybrid pointer generator model helps in removing inaccuracy as well as repetitions. We have used CNN / Daily Mail as our dataset.

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.

scholarly journals SISTEM AUTOMATIC TEXT SUMMARIZATION MENGGUNAKAN ALGORITMA TEXTRANK

MATICS ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 111-116
Author(s):  
Muhammad Adib Zamzam
Keyword(s):  
Text Summarization ◽  
Automatic Text Summarization ◽  
Unit Unit ◽  
Abstractive Summarization ◽  
Automatic Text

Text summarization (perangkuman teks) adalah pendekatan yang bisa digunakan untuk meringkas atau memadatkan teks artikel yang panjang menjadi lebih pendek dan ringkas sehingga hasil rangkuman teks yang relatif lebih pendek bisa mewakilkan teks yang panjang. Automatic Text Summarization adalah perangkuman teks yang dilakukan secara otomatis oleh komputer. Terdapat dua macam algoritma Automatic Text Summarization yaitu Extraction-based summarization dan Abstractive summarization. Algoritma TextRank merupakan algoritma extraction-based atau extractive, dimana ekstraksi di sini berarti memilih unit teks (kalimat, segmen-segmen kalimat, paragraf atau passages), lalu dianggap berisi informasi penting dari dokumen dan menyusun unit-unit (kalimat-kalimat) tersebut dengan cara yang benar. Hasil penelitian dengan input 50 artikel dan hasil rangkuman sebanyak 12,5% dari teks asli menunjukkan bahwa sistem memiliki nilai recall ROUGE 41,659 %. Nilai tertinggi recall ROUGE tertinggi tercatat pada artikel 48 dengan nilai 0,764. Nilai terendah recall ROUGE tercatat pada artikel  37 dengan nilai 0,167.

scholarly journals A Text Abstraction Summary Model Based on BERT Word Embedding and Reinforcement Learning

2019 ◽  
Vol 9 (21) ◽  
pp. 4701 ◽  
Author(s):  
Qicai Wang ◽  
Peiyu Liu ◽  
Zhenfang Zhu ◽  
Hongxia Yin ◽  
Qiuyue Zhang ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Language Processing ◽  
Evaluation Method ◽  
Ground Truth ◽  
Text Summarization ◽  
Word Embedding ◽  
Text Representation ◽  
Daily Mail ◽  
Automatic Text Summarization ◽  
Automatic Text

As a core task of natural language processing and information retrieval, automatic text summarization is widely applied in many fields. There are two existing methods for text summarization task at present: abstractive and extractive. On this basis we propose a novel hybrid model of extractive-abstractive to combine BERT (Bidirectional Encoder Representations from Transformers) word embedding with reinforcement learning. Firstly, we convert the human-written abstractive summaries to the ground truth labels. Secondly, we use BERT word embedding as text representation and pre-train two sub-models respectively. Finally, the extraction network and the abstraction network are bridged by reinforcement learning. To verify the performance of the model, we compare it with the current popular automatic text summary model on the CNN/Daily Mail dataset, and use the ROUGE (Recall-Oriented Understudy for Gisting Evaluation) metrics as the evaluation method. Extensive experimental results show that the accuracy of the model is improved obviously.

scholarly journals Abstractive Summarization: A Survey of the State of the Art

2019 ◽  
Vol 33 ◽  
pp. 9815-9822 ◽  
Author(s):  
Hui Lin ◽  
Vincent Ng
Keyword(s):  
Machine Translation ◽  
State Of The Art ◽  
The State ◽  
Text Summarization ◽  
Abstract Representation ◽  
Automatic Text Summarization ◽  
Input Text ◽  
Gradual Shift ◽  
Abstractive Summarization ◽  
Automatic Text

The focus of automatic text summarization research has exhibited a gradual shift from extractive methods to abstractive methods in recent years, owing in part to advances in neural methods. Originally developed for machine translation, neural methods provide a viable framework for obtaining an abstract representation of the meaning of an input text and generating informative, fluent, and human-like summaries. This paper surveys existing approaches to abstractive summarization, focusing on the recently developed neural approaches.

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.

scholarly journals A Systematic Survey on Multi-document Text Summarization

2021 ◽  
Vol 10 (6) ◽  
pp. 3148-3153
Keyword(s):  
Deep Learning ◽  
Text Summarization ◽  
Evaluation Metrics ◽  
Automatic Process ◽  
Document Summarization ◽  
Text Document ◽  
Automatic Text Summarization ◽  
As Graph ◽  
Abstractive Summarization ◽  
Automatic Text

Automatic text summarization is a technique of generating short and accurate summary of a longer text document. Text summarization can be classified based on the number of input documents (single document and multi-document summarization) and based on the characteristics of the summary generated (extractive and abstractive summarization). Multi-document summarization is an automatic process of creating relevant, informative and concise summary from a cluster of related documents. This paper does a detailed survey on the existing literature on the various approaches for text summarization. Few of the most popular approaches such as graph based, cluster based and deep learning-based summarization techniques are discussed here along with the evaluation metrics, which can provide an insight to the future researchers.

scholarly journals Automatic Text Summarization Using Deep Reinforcement Learning and Beyond

2021 ◽  
Vol 50 (3) ◽  
pp. 458-469
Author(s):  
Gang Sun ◽  
Zhongxin Wang ◽  
Jia Zhao
Keyword(s):  
Information Overload ◽  
Optimization Method ◽  
Text Summarization ◽  
Original Text ◽  
Baseline Model ◽  
Extractive Summarization ◽  
Automatic Text Summarization ◽  
Text Information ◽  
Abstractive Summarization ◽  
Automatic Text

In the era of big data, information overload problems are becoming increasingly prominent. It is challengingfor machines to understand, compress and filter massive text information through the use of artificial intelligencetechnology. The emergence of automatic text summarization mainly aims at solving the problem ofinformation overload, and it can be divided into two types: extractive and abstractive. The former finds somekey sentences or phrases from the original text and combines them into a summarization; the latter needs acomputer to understand the content of the original text and then uses the readable language for the human tosummarize the key information of the original text. This paper presents a two-stage optimization method forautomatic text summarization that combines abstractive summarization and extractive summarization. First,a sequence-to-sequence model with the attention mechanism is trained as a baseline model to generate initialsummarization. Second, it is updated and optimized directly on the ROUGE metric by using deep reinforcementlearning (DRL). Experimental results show that compared with the baseline model, Rouge-1, Rouge-2,and Rouge-L have been increased on the LCSTS dataset and CNN/DailyMail dataset.

scholarly journals Text Summarizing Using NLP

2021 ◽  
Author(s):  
G. Vijay Kumar ◽  
Arvind Yadav ◽  
B. Vishnupriya ◽  
M. Naga Lahari ◽  
J. Smriti ◽  
...  
Keyword(s):  
Text Summarization ◽  
Digital Data ◽  
The Internet ◽  
Short Version ◽  
Keyword Extraction ◽  
Automatic Text Summarization ◽  
Sentence Extraction ◽  
Source Data ◽  
Abstractive Summarization ◽  
Automatic Text

In this era everything is digitalized we can find a large amount of digital data for different purposes on the internet and relatively it’s very hard to summarize this data manually. Automatic Text Summarization (ATS) is the subsequent big one that could simply summarize the source data and give us a short version that could preserve the content and the overall meaning. While the concept of ATS is started long back in 1950’s, this field is still struggling to give the best and efficient summaries. ATS proceeds towards 2 methods, Extractive and Abstractive Summarization. The Extractive and Abstractive methods had a process to improve text summarization technique. Text Summarization is implemented with NLP due to packages and methods in Python. Different approaches are present for summarizing the text and having few algorithms with which we can implement it. Text Rank is what to extractive text summarization and it is an unsupervised learning. Text Rank algorithm also uses undirected graphs, weighted graphs. keyword extraction, sentence extraction. So, in this paper, a model is made to get better result in text summarization with Genism library in NLP. This method improves the overall meaning of the phrase and the person reading it can understand in a better way.

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