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 User-Oriented Summaries Using a PSO Based Scoring Optimization Method

Entropy ◽  
2019 ◽  
Vol 21 (6) ◽  
pp. 617 ◽  
Author(s):  
Augusto Villa-Monte ◽  
Laura Lanzarini ◽  
Aurelio F. Bariviera ◽  
José A. Olivas
Keyword(s):  
Information Overload ◽  
Optimization Method ◽  
Text Summarization ◽  
Binary Representation ◽  
Training Set ◽  
Swarm Optimization ◽  
Automatic Text Summarization ◽  
The Individual ◽  
Improved Accuracy ◽  
Automatic Text

Automatic text summarization tools have a great impact on many fields, such as medicine, law, and scientific research in general. As information overload increases, automatic summaries allow handling the growing volume of documents, usually by assigning weights to the extracted phrases based on their significance in the expected summary. Obtaining the main contents of any given document in less time than it would take to do that manually is still an issue of interest. In this article, a new method is presented that allows automatically generating extractive summaries from documents by adequately weighting sentence scoring features using Particle Swarm Optimization. The key feature of the proposed method is the identification of those features that are closest to the criterion used by the individual when summarizing. The proposed method combines a binary representation and a continuous one, using an original variation of the technique developed by the authors of this paper. Our paper shows that using user labeled information in the training set helps to find better metrics and weights. The empirical results yield an improved accuracy compared to previous methods used in this field.

SGATS: Semantic Graph-based Automatic Text Summarization from Hindi Text Documents

2021 ◽  
Vol 20 (6) ◽  
pp. 1-32
Author(s):  
Manju Lata Joshi ◽  
Nisheeth Joshi ◽  
Namita Mittal
Keyword(s):  
Language Processing ◽  
Semantic Analysis ◽  
Text Summarization ◽  
Original Text ◽  
Theoretic Approach ◽  
Extractive Summarization ◽  
Semantic Graph ◽  
Text Document ◽  
Automatic Text Summarization ◽  
Automatic Text

Creating a coherent summary of the text is a challenging task in the field of Natural Language Processing (NLP). Various Automatic Text Summarization techniques have been developed for abstractive as well as extractive summarization. This study focuses on extractive summarization which is a process containing selected delineative paragraphs or sentences from the original text and combining these into smaller forms than the document(s) to generate a summary. The methods that have been used for extractive summarization are based on a graph-theoretic approach, machine learning, Latent Semantic Analysis (LSA), neural networks, cluster, and fuzzy logic. In this paper, a semantic graph-based approach SGATS (Semantic Graph-based approach for Automatic Text Summarization) is proposed to generate an extractive summary. The proposed approach constructs a semantic graph of the original Hindi text document by establishing a semantic relationship between sentences of the document using Hindi Wordnet ontology as a background knowledge source. Once the semantic graph is constructed, fourteen different graph theoretical measures are applied to rank the document sentences depending on their semantic scores. The proposed approach is applied to two data sets of different domains of Tourism and Health. The performance of the proposed approach is compared with the state-of-the-art TextRank algorithm and human-annotated summary. The performance of the proposed system is evaluated using widely accepted ROUGE measures. The outcomes exhibit that our proposed system produces better results than TextRank for health domain corpus and comparable results for tourism corpus. Further, correlation coefficient methods are applied to find a correlation between eight different graphical measures and it is observed that most of the graphical measures are highly correlated.

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 Framework for Word Embedding Based Automatic Text Summarization and Evaluation

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 78 ◽  
Author(s):  
Tulu Tilahun Hailu ◽  
Junqing Yu ◽  
Tessfu Geteye Fantaye
Keyword(s):  
Text Summarization ◽  
Evaluation Framework ◽  
Word Embedding ◽  
Evaluation Metrics ◽  
Original Text ◽  
Automatic Evaluation ◽  
Source Text ◽  
Automatic Text Summarization ◽  
Automatic Text

Text summarization is a process of producing a concise version of text (summary) from one or more information sources. If the generated summary preserves meaning of the original text, it will help the users to make fast and effective decision. However, how much meaning of the source text can be preserved is becoming harder to evaluate. The most commonly used automatic evaluation metrics like Recall-Oriented Understudy for Gisting Evaluation (ROUGE) strictly rely on the overlapping n-gram units between reference and candidate summaries, which are not suitable to measure the quality of abstractive summaries. Another major challenge to evaluate text summarization systems is lack of consistent ideal reference summaries. Studies show that human summarizers can produce variable reference summaries of the same source that can significantly affect automatic evaluation metrics scores of summarization systems. Humans are biased to certain situation while producing summary, even the same person perhaps produces substantially different summaries of the same source at different time. This paper proposes a word embedding based automatic text summarization and evaluation framework, which can successfully determine salient top-n sentences of a source text as a reference summary, and evaluate the quality of systems summaries against it. Extensive experimental results demonstrate that the proposed framework is effective and able to outperform several baseline methods with regard to both text summarization systems and automatic evaluation metrics when tested on a publicly available dataset.

scholarly journals Developing a new approach to summarize Arabic text automatically using syntactic and semantic analysis

2020 ◽  
Vol 9 (2) ◽  
pp. 342
Author(s):  
Amal Alkhudari
Keyword(s):  
Language Processing ◽  
Automatic System ◽  
Semantic Analysis ◽  
Text Summarization ◽  
Original Text ◽  
Arabic Text ◽  
Wide Spread ◽  
New Approach ◽  
Automatic Text Summarization ◽  
Automatic Text

Due to the wide spread information and the diversity of its sources, there is a need to produce an accurate text summary with the least time and effort. This summary must  preserve key information content and overall meaning of the original text. Text summarization is one of the most important applications of Natural Language Processing (NLP). The goal of automatic text summarization is to create summaries that are similar to human-created ones. However, in many cases, the readability of created summaries is not satisfactory,   because the summaries do not consider the meaning of the words and do not cover all the semantically relevant aspects of data. In this paper we use syntactic and semantic analysis to propose an automatic system of Arabic texts summarization. This system is capable of understanding the meaning of information and retrieves only the relevant part. The effectiveness and evaluation of the proposed work are demonstrated under EASC corpus using Rouge measure. The generated summaries will be compared against those done by human and precedent researches.  

A New Biomimetic Method Based on the Power Saves of Social Bees for Automatic Summaries of Texts by Extraction

2015 ◽  
Vol 7 (1) ◽  
pp. 18-38 ◽  
Author(s):  
Mohamed Amine Boudia ◽  
Reda Mohamed Hamou ◽  
Abdelmalek Amine ◽  
Amine Rahmani
Keyword(s):  
Text Summarization ◽  
Second Step ◽  
Original Text ◽  
Simple Majority ◽  
New Approach ◽  
Social Bees ◽  
Automatic Text Summarization ◽  
Final Layer ◽  
Biomimetic Method ◽  
Automatic Text

In this paper, the authors propose a new approach for automatic text summarization by extraction based on Saving Energy Function where the first step constitute to use two techniques of extraction: scoring of phrases, and similarity that aims to eliminate redundant phrases without losing the theme of the text. While the second step aims to optimize the results of the previous layer by the metaheuristic based on Bee Algorithm, the objective function of the optimization is to maximize the sum of similarity between phrases of the candidate summary in order to keep the theme of the text, minimize the sum of scores in order to increase the summarization rate, this optimization also will give a candidate's summary where the order of the phrases changes compared to the original text. The third and final layer aims to choose the best summary from the candidate summaries generated by bee optimization, the authors opted for the technique of voting with a simple majority.

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.

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