Text summarization using concept graph and BabelNet knowledge base

Rashidghalam, Haniyeh; Taherkhani, Mina; Mahmoudi, Fariborz

doi:10.1109/rios.2016.7529500

Cited by 10 publications

(4 citation statements)

References 12 publications

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“…[ 8 ], a multi-layered graph modeling and text summarization study was conducted for the purpose of biomedical text summarization. Numerous productive studies with similar assumptions are also evident in the current literature [ 6 , [18] , [19] , [20] , [21] , [22] ].…”

Section: Related Researchmentioning

confidence: 61%

The power of graphs in medicine: Introducing BioGraphSum for effective text summarization

Hark

2024

Heliyon

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Section: Related Researchmentioning

confidence: 61%

The power of graphs in medicine: Introducing BioGraphSum for effective text summarization

Hark

2024

Heliyon

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“…The approach proposed gives a 50% overview of the original text and gives a good result, while an original text is 25% overviewed. Rashidghalam et al (2016) used The BabelNet Knowledge Base and its concept diagram provide a text summary system. The proposed approach extracts concepts of words that use the Babel Net knowledge base and produces concept charts and evaluates phrases based on the concepts and graphs that result.…”

Section: Literature Reviewmentioning

confidence: 99%

Abstractive method-based Text Summarization using Bidirectional Long Short-Term Memory and Pointer Generator Mode

Tripathy¹,

Sharmila

2023

JART

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With the rise of the Internet, we now have a lot of information at our disposal. We 're swamped from many sources — news, social media, to name a few, office emails. This paper addresses the problem of reading through such extensive information by summarizing it using text summarizer based on Abstractive Summarization using deep learning models, i.e. using bidirectional Long Short-Term Memory (LSTM) networks and Pointer Generator mode. The LSTM model (which is a modification of the Recurrent Neural Network) is trained and tested on the Amazon Fine Food Review dataset using the Bahadau Attention Model Decoder with the use of Conceptnet Numberbatch embeddings that are very similar and better to GloVe. Pointer Generator mode is trained and tested by the CNN / Daily Mail dataset and the model uses both Decoder and Attention inputs. But due 2 major problems in LSTM model like the inability of the network to copy facts and repetition of words the second method is, i.e., Pointer Generator mode is used. This paper in turn aims to provide an analysis on both the models to provide a better understanding of the working of the models to enable to create a strong text summarizer. The main purpose is to provide reliable summaries of datasets or uploaded files, depending on the choice of the user. Unnecessary sentences will be rejected in order to obtain the most important sentences.

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“…Similar concepts are extracted and placed in a related community. The sentences are rated with respect to these concepts and communities, and a summary is produced [66].…”

Section: E a R L Y B I R Dmentioning

confidence: 99%

Survey of Scientific Document Summarization Techniques

K¹,

Mathew²

2020

csci

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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.

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Text summarization using concept graph and BabelNet knowledge base

Cited by 10 publications

References 12 publications

The power of graphs in medicine: Introducing BioGraphSum for effective text summarization

The power of graphs in medicine: Introducing BioGraphSum for effective text summarization

Abstractive method-based Text Summarization using Bidirectional Long Short-Term Memory and Pointer Generator Mode

Survey of Scientific Document Summarization Techniques

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