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 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.  

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 A boundary-based tokenization technique for extractive text summarization

2021 ◽  
Vol 11 (2) ◽  
pp. 303-312
Author(s):  
Nnaemeka M Oparauwah ◽  
Juliet N Odii ◽  
Ikechukwu I Ayogu ◽  
Vitalis C Iwuchukwu
Keyword(s):  
Academic Research ◽  
Text Summarization ◽  
Text Documents ◽  
Health Records ◽  
Extractive Summarization ◽  
Content Creation ◽  
Text Document ◽  
Automatic Text Summarization ◽  
Automatic Text ◽  
Selection Of

The need to extract and manage vital information contained in copious volumes of text documents has given birth to several automatic text summarization (ATS) approaches. ATS has found application in academic research, medical health records analysis, content creation and search engine optimization, finance and media. This study presents a boundary-based tokenization method for extractive text summarization. The proposed method performs word tokenization by defining word boundaries in place of specific delimiters. An extractive summarization algorithm was further developed based on the proposed boundary-based tokenization method, as well as word length consideration to control redundancy in summary output. Experimental results showed that the proposed approach enhanced word tokenization by enhancing the selection of appropriate keywords from text document to be used for summarization.

scholarly journals Single Document Text Summarization of a Resource-Poor Language using an Unsupervised Technique

2019 ◽  
Vol 9 (1) ◽  
pp. 6278-6281
Keyword(s):  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Singular Value ◽  
Text Summarization ◽  
Text Document ◽  
Automatic Text Summarization ◽  
Resource Poor ◽  
Value Decomposition ◽  
Scarcity Of Resources ◽  
Automatic Text

Automatic text summarization of a resource-poor language is a challenging task. Unsupervised extractive techniques are often preferred for such languages due to scarcity of resources. Latent Semantic Analysis (LSA) is an unsupervised technique which automatically identifies semantically important sentences from a text document. Two methods based on Latent Semantic Analysis have been evaluated on two datasets of a resource-poor language using Singular Value Decomposition (SVD) on different vector-space models. The performance of the methods is evaluated using ROUGE-L scores obtained by comparing the system generated summaries with human generated model summaries. Both the methods are found to be performing better for shorter documents than longer ones.

scholarly journals Text Summarization Approaches Using Machine Learning & LSTM

2021 ◽  
Vol 11 (4) ◽  
pp. 5010-5026
Author(s):  
Neeraj Kumar Sirohi ◽  
Dr. Mamta Bansal ◽  
Dr.S.N. Rajan Rajan
Keyword(s):  
Literature Survey ◽  
Text Summarization ◽  
The Other ◽  
Original Text ◽  
Text Document ◽  
Automatic Text Summarization ◽  
Textual Data ◽  
Other Information ◽  
Future Work ◽  
Automatic Text

Due to the massive amount of online textual data generated in a diversity of social media, web, and other information-centric applications. To select the vital data from the large text, need to study the full article and generate summary also not loose critical information of text document this process is called summarization. Text summarization is done either by human which need expertise in that area, also very tedious and time consuming. second type of summarization is done through system which is known as automatic text summarization which generate summary automatically. There are mainly two categories of Automatic text summarizations that is abstractive and extractive text summarization. Extractive summary is produced by picking important and high rank sentences and word from the text document on the other hand the sentences and word are present in the summary generated through Abstractive method may not present in original text. This article mainly focuses on different ATS (Automatic text summarization) techniques that has been instigated in the present are argue. The paper begin with a concise introduction of automatic text summarization, then closely discussed the innovative developments in extractive and abstractive text summarization methods, and then transfers to literature survey, and it finally sum-up with the proposed techniques using LSTM with encoder Decoder for abstractive text summarization are discussed along with some future work directions.

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 Document Summarization with VHTM: Variational Hierarchical Topic-Aware Mechanism

2020 ◽  
Vol 34 (05) ◽  
pp. 7740-7747 ◽  
Author(s):  
Xiyan Fu ◽  
Jun Wang ◽  
Jinghan Zhang ◽  
Jinmao Wei ◽  
Zhenglu Yang
Keyword(s):  
Language Processing ◽  
Topic Model ◽  
Research Field ◽  
Text Summarization ◽  
Superior Performance ◽  
The Past ◽  
Automatic Text Summarization ◽  
Latent Topics ◽  
Further Development ◽  
Automatic Text

Automatic text summarization focuses on distilling summary information from texts. This research field has been considerably explored over the past decades because of its significant role in many natural language processing tasks; however, two challenging issues block its further development: (1) how to yield a summarization model embedding topic inference rather than extending with a pre-trained one and (2) how to merge the latent topics into diverse granularity levels. In this study, we propose a variational hierarchical model to holistically address both issues, dubbed VHTM. Different from the previous work assisted by a pre-trained single-grained topic model, VHTM is the first attempt to jointly accomplish summarization with topic inference via variational encoder-decoder and merge topics into multi-grained levels through topic embedding and attention. Comprehensive experiments validate the superior performance of VHTM compared with the baselines, accompanying with semantically consistent topics.

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.

Sign in / Sign up

Export Citation Format

Share Document