scholarly journals Implementing Supervised Approach to Summarization of Research Papers

2020 ◽  
Vol 6 (12) ◽  
pp. 398-401
Author(s):  
Shaguna Awasth
Keyword(s):  
Supervised Learning ◽  
Research Paper ◽  
Text Summarization ◽  
Research Papers ◽  
Global Context ◽  
Automatic Text Summarization ◽  
Automatic Text ◽  
Intuitive Model

Using automatic text summarization we can reduce a document to its main information or to what is known as crux of the document .Recent research in this zone has zeroed in on neural ways to deal with summarisation, which can be very data hungry. This paper aims to explore a quicker way by implementing a supervised-learning based extractive summarisation system for the summarisation of research papers. This paper also explores the possibility of any section, in a research paper being the prime section to generate summaries by utilizing ROUGE scores. An easy to implement and intuitive model is developed using glove embeddings and doc2vec to encode sentences and documents in their local and global context producing grammatically coherent summaries.

scholarly journals Survey of Scientific Document Summarization Techniques

2020 ◽  
Vol 21 (2) ◽  
Author(s):  
Sheena Kurian K ◽  
Sheena Mathew
Keyword(s):  
Text Summarization ◽  
Exponential Rate ◽  
Research Papers ◽  
Document Summarization ◽  
Automatic Text Summarization ◽  
Scientific Document Summarization ◽  
Pros And Cons ◽  
Comparison Of The Results ◽  
Evaluation Techniques ◽  
Automatic Text

The number of scientic or research papers published every year is growing at an exponential rate, which has led to an intensive research in scientic document summarization. The different methods commonly used in automatic text summarization are discussed in this paper with their pros and cons. Commonly used evaluation techniques and datasets in this field are also discussed. Rouge and Pyramid scores of the different methods are tabulated for easy comparison of the results.

A Quantum-Inspired Genetic Algorithm for Extractive Text Summarization

2021 ◽  
Vol 10 (2) ◽  
pp. 42-60
Author(s):  
Khadidja Chettah ◽  
Amer Draa
Keyword(s):  
Genetic Algorithm ◽  
State Of The Art ◽  
Text Summarization ◽  
Automated System ◽  
Evaluation Metrics ◽  
Document Summarization ◽  
Automatic Text Summarization ◽  
Reference Methods ◽  
Textual Data ◽  
Automatic Text

Automatic text summarization has recently become a key instrument for reducing the huge quantity of textual data. In this paper, the authors propose a quantum-inspired genetic algorithm (QGA) for extractive single-document summarization. The QGA is used inside a totally automated system as an optimizer to search for the best combination of sentences to be put in the final summary. The presented approach is compared with 11 reference methods including supervised and unsupervised summarization techniques. They have evaluated the performances of the proposed approach on the DUC 2001 and DUC 2002 datasets using the ROUGE-1 and ROUGE-2 evaluation metrics. The obtained results show that the proposal can compete with other state-of-the-art methods. It is ranked first out of 12, outperforming all other algorithms.

scholarly journals Prediction and Analysis of Extracting Relations using Spacy Model

2020 ◽  
Vol 8 (6) ◽  
pp. 3281-3287
Keyword(s):  
Natural Language ◽  
Information Extraction ◽  
Performance Measures ◽  
Text Summarization ◽  
Language Understanding ◽  
Language Generation ◽  
Automatic Text Summarization ◽  
Structured Information ◽  
Automatic Text ◽  
F Measure

Text is an extremely rich resources of information. Each and every second, minutes, peoples are sending or receiving hundreds of millions of data. There are various tasks involved in NLP are machine learning, information extraction, information retrieval, automatic text summarization, question-answered system, parsing, sentiment analysis, natural language understanding and natural language generation. The information extraction is an important task which is used to find the structured information from unstructured or semi-structured text. The paper presents a methodology for extracting the relations of biomedical entities using spacy. The framework consists of following phases such as data creation, load and converting the data into spacy object, preprocessing, define the pattern and extract the relations. The dataset is downloaded from NCBI database which contains only the sentences. The created model evaluated with performance measures like precision, recall and f-measure. The model achieved 87% of accuracy in retrieving of entities relation.

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