Document Summarization using TextRank and Semantic Network

Ashari, Ahmad; Riasetiawan, Mardhani

doi:10.5815/ijisa.2017.11.04

Cited by 8 publications

(7 citation statements)

References 10 publications

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“…Even if English is considered a primary dominating language in a different kind of information business, the www database has become a genuine information source in other languages. Numerous methods and approaches for successful information retrieval have been developed, with search engines serving as notable examples [8], [20]. Information retrieval is widely used in various fields such as research, education, business, ecommerce, and entertainment.…”

Section: Literature Reviewmentioning

confidence: 99%

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An Efficient Information Extraction Mechanism with Page Ranking and a Classification Strategy based on Similarity Learning of Web Text Documents

Thota,

Kumar,

Koti

et al. 2023

IJRITCC

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Users have recently had more access to information thanks to the growth of the www information system. In these situations, search engines have developed into an essential tool for consumers to find information in a big space. The difficulty of handling this wealth of knowledge grows more difficult every day. Although search engines are crucial for information gathering, many of the results they offer are not required by the user because they are ranked according on user string matches. As a result, there were semantic disparities between the terms used in the user inquiry and the importance of catch phrases in the results. The problem of grouping relevant information into categories of related topics hasn't been solved. A Ranking Based Similarity Learning Approach and SVM based classification frame work of web text to estimate the semantic comparison between words to improve extraction of information is proposed in the work. The results of the experiment suggest improvisation in order to obtain better results by retrieving more relevant results.

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Section: Literature Reviewmentioning

confidence: 99%

“…Xuan Wu et al [20] studied semantic connections in social tagging systems, exploring links between tags, words, and both tags and words. Using tags and functions extracted from words, they generated three similarity graphs.…”

Section: Literature Reviewmentioning

confidence: 99%

An Efficient Information Extraction Mechanism with Page Ranking and a Classification Strategy based on Similarity Learning of Web Text Documents

Thota,

Kumar,

Koti

et al. 2023

IJRITCC

View full text Add to dashboard Cite

show abstract

“…This graph is passed as an input to the TextRank algorithm which generates the required summary. Similar approach is followed by (Ashari and Riasetiawan, 2017) which uses the power of TextRank and semantic networks to form extractive summaries which bear the semantic relations. Some of the works like (Nallapati et al, 2017), (Al-Sabahi et al, 2018 use capabilities of neural networks to semantically extract the information from the description and present it in human readable form.…”

Section: Related Workmentioning

confidence: 99%

CNLP-NITS @ LongSumm 2021: TextRank Variant for Generating Long Summaries

Kaushik¹,

Khilji²,

Sinha³

2021

Proceedings of the Second Workshop on Scholarly Document Processing

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The huge influx of published papers in the field of machine learning makes the task of summarization of scholarly documents vital, not just to eliminate the redundancy but also to provide a complete and satisfying crux of the content. We participated in LongSumm 2021: The 2 nd Shared Task on Generating Long Summaries for scientific documents, where the task is to generate long summaries for scientific papers provided by the organizers. This paper discusses our extractive summarization approach to solve the task. We used TextRank algorithm with the BM25 score as a similarity function. Even after being a graph-based ranking algorithm that does not require any learning, Tex-tRank produced pretty decent results with minimal compute power and time. We attained 3 rd rank according to ROUGE-1 scores (0.5131 for F-measure and 0.5271 for recall) and performed decently as shown by the ROUGE-2 scores.

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“…This graph is passed as an input to the TextRank algorithm which generates the required summary. Similar approach is followed by (Ashari and Riasetiawan, 2017) which uses the power of TextRank and semantic networks to form extractive summaries which bear the semantic relations. Some of the works like use capabilities of neural networks to semantically extract the information from the description and present it in human readable form.…”

Section: Related Workmentioning

confidence: 99%

Proceedings of the Second Workshop on Scholarly Document Processing

2021

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Most work on scholarly document processing assumes that the information processed is trustworthy and factually correct. However, this is not always the case. There are two core challenges, which should be addressed: 1) ensuring that scientific publications are credible -e.g. that claims are not made without supporting evidence, and that all relevant supporting evidence is provided; and 2) that scientific findings are not misrepresented, distorted or outright misreported when communicated by journalists or the general public. I will present some first steps towards addressing these problems and outline remaining challenges. BiologyWood Frogs (Rana sylvatica) are a charismatic species of frog common in much of North America. They breed in explosive choruses over a few nights in late winter to early spring. The incidence in Wood Frogs was associated with a die-off of frogs during the breeding chorus in the Sylamore District of the Ozark National Forest in Arkansas (Trauth et al., 2000). Computer ScienceLand use or cover change is a direct reflection of human activity, such as land use, urban expansion, and architectural planning, on the earth's surface caused by urbanization [1]. Remote sensing images are important data sources that can efficiently detect land changes. Meanwhile, remote sensing image-based change detection is the change identification of surficial objects or geographic phenomena through the remote observation of two or more different phases [2].

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Document Summarization using TextRank and Semantic Network

Cited by 8 publications

References 10 publications

An Efficient Information Extraction Mechanism with Page Ranking and a Classification Strategy based on Similarity Learning of Web Text Documents

An Efficient Information Extraction Mechanism with Page Ranking and a Classification Strategy based on Similarity Learning of Web Text Documents

CNLP-NITS @ LongSumm 2021: TextRank Variant for Generating Long Summaries

Proceedings of the Second Workshop on Scholarly Document Processing

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