scholarly journals Abstractive Document Summarization with Word Embedding Reconstruction

2021 ◽  
Author(s):  
Jingyi You ◽  
◽  
Chenlong Hu ◽  
Hidetaka Kamigaito ◽  
Hiroya Takamura ◽  
...  
Keyword(s):  
Word Embedding ◽  
Document Summarization

Text document summarization using word embedding

2020 ◽  
Vol 143 ◽  
pp. 112958 ◽  
Author(s):  
Mudasir Mohd ◽  
Rafiya Jan ◽  
Muzaffar Shah
Keyword(s):  
Word Embedding ◽  
Document Summarization ◽  
Text Document

scholarly journals SUMMARIZING INDONESIAN NEWS ARTICLES USING GRAPH CONVOLUTIONAL NETWORK

2019 ◽  
Author(s):  
Garmastewira Garmastewira ◽  
Masayu Leylia Khodra
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Recall Score ◽  
Word Embedding ◽  
Graph Representation ◽  
Representation System ◽  
Convolutional Network ◽  
Document Summarization ◽  
Main Components ◽  
Representation Types

Multi-document summarization transforms a set of related documents into one concise summary. Existing Indonesian news articles summarizations do not take relationships between sentences into account and heavily depends on Indonesian language tools and resources. In this paper, we employ Graph Convolutional Network (GCN) which accepts word embedding sequence and sentence relationship graph as input for Indonesian news articles summarization. Our system is comprised of four main components, which are preprocess, graph construction, sentence scoring, and sentence selection components. Sentence scoring component is a neural network that uses Recurrent Neural Network (RNN) and GCN to produce the scores of all sentences. We use three different representation types for the sentence relationship graph. Sentence selection component then generates summary with two different techniques, which are by greedily choosing sentences with the highest scores and by using Maximum Marginal Relevance (MMR) technique. The evaluation shows that GCN summarizer with Personalized Discourse Graph (PDG) graph representation system achieves the best results with average ROUGE-2 recall score of 0.370 for 100-word summary and 0.378 for 200-word summary. Sentence selection using greedy technique gives better results for generating 100-word summary, while MMR performs better for generating 200-word summary.  

A Simple Word Embedding Model for Lexical Substitution

2015 ◽  
Author(s):  
Oren Melamud ◽  
Omer Levy ◽  
Ido Dagan
Keyword(s):  
Word Embedding ◽  
Lexical Substitution

An Automatic Text Summarization Method with the Concern of Covering Complete Formation

2020 ◽  
Vol 13 (5) ◽  
pp. 977-986
Author(s):  
Srinivasa Rao Kongara ◽  
Dasika Sree Rama Chandra Murthy ◽  
Gangadhara Rao Kancherla
Keyword(s):  
Research Method ◽  
Research Work ◽  
Fuzzy Rule ◽  
Text Summarization ◽  
Document Summarization ◽  
Summarization Method ◽  
Overall Evaluation ◽  
Multiple Documents ◽  
Rule System ◽  
Value Decomposition

Background: Text summarization is the process of generating a short description of the entire document which is more difficult to read. This method provides a convenient way of extracting the most useful information and a short summary of the documents. In the existing research work, this is focused by introducing the Fuzzy Rule-based Automated Summarization Method (FRASM). Existing work tends to have various limitations which might limit its applicability to the various real-world applications. The existing method is only suitable for the single document summarization where various applications such as research industries tend to summarize information from multiple documents. Methods: This paper proposed Multi-document Automated Summarization Method (MDASM) to introduce the summarization framework which would result in the accurate summarized outcome from the multiple documents. In this work, multi-document summarization is performed whereas in the existing system only single document summarization was performed. Initially document clustering is performed using modified k means cluster algorithm to group the similar kind of documents that provides the same meaning. This is identified by measuring the frequent term measurement. After clustering, pre-processing is performed by introducing the Hybrid TF-IDF and Singular value decomposition technique which would eliminate the irrelevant content and would result in the required content. Then sentence measurement is one by introducing the additional metrics namely Title measurement in addition to the existing work metrics to accurately retrieve the sentences with more similarity. Finally, a fuzzy rule system is applied to perform text summarization. Results: The overall evaluation of the research work is conducted in the MatLab simulation environment from which it is proved that the proposed research method ensures the optimal outcome than the existing research method in terms of accurate summarization. MDASM produces 89.28% increased accuracy, 89.28% increased precision, 89.36% increased recall value and 70% increased the f-measure value which performs better than FRASM. Conclusion: The summarization processes carried out in this work provides the accurate summarized outcome.

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