Recurrent Neural Network based Text Summarization Techniques by Word Sequence Generation

2021 ◽  
Author(s):  
R. Subha Shini ◽  
V.D. Ambeth Kumar
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Text Summarization ◽  
Sequence Generation ◽  
Word Sequence

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 PERINGKAS OTOMATIS ABSTRAKTIF DENGAN MENGGUNAKAN RECURRENT NEURAL NETWORK

2018 ◽  
Vol 2 (1) ◽  
pp. 65 ◽  
Author(s):  
Kuncoro Yoko ◽  
Viny Christanti Mawardi ◽  
Janson Hendryli
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Model System ◽  
Text Summarization ◽  
Word Embedding ◽  
System Evaluation ◽  
Data Set ◽  
Hidden States ◽  
Bahasa Indonesia

Abstractive Text Summarization try to creates a shorter version of a text while preserve its meaning. We try to use Recurrent Neural Network (RNN) to create summaries of Bahasa Indonesia text. We get corpus from Detik dan Kompas site news. We used word2vec to create word embedding from our corpus then train our data set with RNN to create a model. This model used to generate news. We search the best model by changing word2vec size and RNN hidden states. We use system evaluation and Q&A Evaluation to evaluate our model. System evaluation showed that model with 6457 data set, 200 word2vec size, and 256 RNN hidden states gives best accuracy for 99.8810%. This model evaluated by Q&A Evaluation. Q&A Evaluation showed that the model gives 46.65% accurary.

scholarly journals Intelligent Content Conciser using Pointer Generated Network

2019 ◽  
Vol 8 (4) ◽  
pp. 11810-11814
Keyword(s):  
Neural Network ◽  
Hybrid Model ◽  
Recurrent Neural Network ◽  
End Users ◽  
Text Summarization ◽  
Document Similarity ◽  
Training Activity ◽  
Working Model ◽  
Proposed Model ◽  
Efficient Learning

The combination of best suited architecture and successful algorithm results in the increased nature of efficient learning among the end users. To increase the number of quality learner’s text summarization provides the best initiative among the readers and learners. As words and sentences comprise a document, document summarization finds diverse words with different sets of synonyms by performing training activity for the process. The S2S(Sequence to Sequence) training mechanism describes the embedding way of sentences and documents. The pointer generation enhances the new hybrid model for summary extraction. The proposed model implements attention mechanism and uses Recurrent Neural Network with LSTM cells at encoder and decoder. The working model focuses on many factors for summary extraction such as sentence/document similarity, repeatedness, indexing and sentence-context richness. It also keeps track of summarized text using coverage to avoid repetition

scholarly journals Implementasi Metode Reccurrent Neural Network pada Text Summarization dengan Teknik Abstraktif

2019 ◽  
Vol 6 (4) ◽  
pp. 377
Author(s):  
Kasyfi Ivanedra ◽  
Metty Mustikasari
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Text Summarization ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
F Measure

<p>Text Summarization atau peringkas text merupakan salah satu penerapan Artificial Intelligence (AI) dimana komputer dapat meringkas text pada suatu kalimat atau artikel menjadi lebih sederhana dengan tujuan untuk mempermudah manusia dalam mengambil kesimpulan dari artikel yang panjang tanpa harus membaca secara keseluruhan. Peringkasan teks secara otomatis dengan menggunakan teknik Abstraktif memiliki kemampuan meringkas teks lebih natural sebagaimana manusia meringkas dibandingkan dengan teknik ekstraktif yang hanya menyusun kalimat berdasarkan frekuensi kemunculan kata. Untuk dapat menghasilkan sistem peringkas teks dengan metode abstraktif, membutuhkan metode Recurrent Neural Network (RNN) yang memiliki sistematika perhitungan bobot secara berulang. RNN merupakan bagian dari Deep Learning dimana nilai akurasi yang dihasilkan dapat lebih baik dibandingkan dengan jaringan saraf tiruan sederhana karena bobot yang dihitung akan lebih akurat mendekati persamaan setiap kata. Jenis RNN yang digunakan adalah LSTM (Long Short Term Memory) untuk menutupi kekurangan pada RNN yang tidak dapat menyimpan memori untuk dipilah dan menambahkan mekanisme Attention agar setiap kata dapat lebih fokus pada konteks. Penelitian ini menguji performa sistem menggunakan Precision, Recall, dan F-Measure dengan membandingan hasil ringkasan yang dihasilkan oleh sistem dan ringkasan yang dibuat oleh manusia. Dataset yang digunakan adalah data artikel berita dengan jumlah total artikel sebanyak 4515 buah artikel. Pengujian dibagi berdasarkan data dengan menggunakan Stemming dan dengan teknik Non-stemming. Nilai rata-rata recall artikel berita non-stemming adalah sebesar 41%, precision sebesar 81%, dan F-measure sebesar 54,27%. Sedangkan nilai rata-rata recall artikel berita dengan teknik stemming sebesar 44%, precision sebesar 88%, dan F-measure sebesar 58,20 %.</p><p><em><strong>Abstract</strong></em></p><p class="Judul2"><em>Text Summarization is the application of Artificial Intelligence (AI) where the computer can summarize text of article to make it easier for humans to draw conclusions from long articles without having to read entirely. Abstractive techniques has ability to summarize the text more naturally as humans summarize. The summary results from abstractive techinques are more in context when compared to extractive techniques which only arrange sentences based on the frequency of occurrence of the word. To be able to produce a text summarization system with an abstractive techniques, it is required Deep Learning by using the Recurrent Neural Network (RNN) rather than simple Artificial Neural Network (ANN) method which has a systematic calculation of weight repeatedly in order to improve accuracy. The type of RNN used is LSTM (Long Short Term Memory) to cover the shortcomings of the RNN which cannot store memory to be sorted and add an Attention mechanism so that each word can focus more on the context.This study examines the performance of Precision, Recall, and F-Measure from the comparison of the summary results produced by the system and summaries made by humans. The dataset used is news article data with 4515 articles. Testing was divided based on data using Stemming and Non-stemming techniques.</em> <em>The average recall value of non-stemming news articles is 41%, precision is 81%, and F-measure is 54.27%. While the average value of recall of news articles with stemming technique is 44%, precision is 88%, and F-measure is 58.20%.</em></p><p><em><strong><br /></strong></em></p>

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

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