Improving Triangle-Graph Based Text Summarization using Hybrid Similarity Function

AL-Khassawneh, Yazan Alaya; Salim, Naomie; Jarrah, Mutasem

doi:10.17485/ijst/2017/v10i8/108907

Cited by 9 publications

(3 citation statements)

References 37 publications

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“…TextRank is an efficient method for calculating the importance of text, which has been successfully applied to text summarization tasks (Al‐Khassawneh et al, 2017; Mao et al, 2019). Usually, the TextRank algorithm uses sentences in a text as nodes and the similarity between sentences as the weight of edges between nodes, to construct a graph.…”

Section: Methodsmentioning

confidence: 99%

An unsupervised policy relevance scoring method: Taking Chinese social security policies as the application case

Sun

Huang

2022

Expert Systems

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Organizing and managing policy documents (PDs) issued to the public in a good way can improve the efficiency of government employees and make it easier for the public to find the needed policy information. However, existing PDs are organized only by dates and manually defined categories; besides, PDs issued by different government branches are isolated from each other. These problems make it challenging and time-consuming for the public to find the needed policy information. We argue that implicit links should be established between PDs based on their relevance, thus helping the public find the needed policy information efficiently. To this end, we propose an unsupervised relevance scoring method for PDs consist six modules, taking Chinese social security policies as the application case. The method combines the TextRank algorithm, TF-IDF representation, mutual information and left-right information entropy algorithm, and BERT. The method can decrease the interference of noisy words in PDs to relevance scoring. In addition, the method can consider multiple features of PDs simultaneously so that the measure of relevance can be more comprehensive. The method is not driven by domain-specific labelled data, hence can be easily generalized to PDs in various domains. We construct a dataset containing 5000 Chinese social security policies and then conduct experiments on it to evaluate our method. Experimental results show that our method is feasible and can bring convenience to government agencies and the public to a certain extent. Furthermore, our method achieves more than a 3% improvement in evaluation results on test tasks than the methods with a similar purpose in the legal AI community.

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Section: Methodsmentioning

confidence: 99%

An unsupervised policy relevance scoring method: Taking Chinese social security policies as the application case

Sun

Huang

2022

Expert Systems

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“…The researchers in ref. [12] used a graph-based approach to extractive summarization. The later researchers suggested a brand-new summarizing technique based on a hybrid modeling graph.…”

Section: Related Workmentioning

confidence: 99%

Extractive Arabic Text Summarization-Graph-Based Approach

AL-Khassawneh

Hanandeh

2023

Electronics

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With the noteworthy expansion of textual data sources in recent years, easy, quick, and precise text processing has become a challenge for key qualifiers. Automatic text summarization is the process of squeezing text documents into shorter summaries to facilitate verification of their basic contents, which must be completed without losing vital information and features. The most difficult information retrieval task is text summarization, particularly for Arabic. In this research, we offer an automatic, general, and extractive Arabic single document summarizing approach with the goal of delivering a sufficiently informative summary. The proposed model is based on a textual graph to generate a coherent summary. Firstly, the original text is converted to a textual graph using a novel formulation that takes into account sentence relevance, coverage, and diversity to evaluate each sentence using a mix of statistical and semantic criteria. Next, a sub-graph is built to reduce the size of the original text. Finally, unwanted and less weighted phrases are removed from the summarized sentences to generate a final summary. We used Recall-Oriented Research to Evaluate Main Idea (RED) as an evaluative metric to review our proposed technique and compare it with the most advanced methods. Finally, a trial on the Essex Arabic Summary Corpus (EASC) using the ROUGE index showed promising results compared with the currently available methods.

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“…In Ref. [30], the researchers adopted a graph-based approach for carrying out an extractive summarization. The latter researchers proposed a new method for summarization.…”

Section: A Graph-based Text Summarizationmentioning

confidence: 99%

The use of Semantic Role Labelling with Triangle-Graph Based Text Summarization

AL-Khassawneh¹

2020

IJETER

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Text summarization aims to create a condensed version of a text, thus retaining the original ideas. In particular, the textual content on the web is increasing at an exponential pace. The ability to interpret valuable information from such a vast volume of data is an essential undertaking and requires an automated system to assist with the current information repository. Text summarization systems aim to help reducing content by retaining the relevant material and filtering out non-important sections of the text. There are two basic approaches in the text summarization systems. Single document summarization is outlined in the first method. In other words, the method uses one document as input and generates a summary version as output. An alternative approach is to take many documents as input and generate a single summary document as output. In terms of performance, the summarization systems are often classified into two groups. One method will be to remove exact sentences from the original document in order to create a summary production. An alternative would be a more nuanced approach in which the text rendered is rephrased version of the original document. A formal semantic representation is provided in this paper, which can capture the algorithms and the text meaning to allow mapping the text documents with the meaning representation. This semantic representation, which focuses on establishing relations amongst concepts of text constituents, is considered to enhance the performance of the single-document extractive summarisation processes. To see how text constituents (i.e. words, phrases and morphemes) are related, a graph semantic model (GSM) was used, which is built using a syntactic and semantic analysis. Semantic Role Labelling (SRL) is the fundamental relationship that a contributor has with the main verb in a sentence.

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Improving Triangle-Graph Based Text Summarization using Hybrid Similarity Function

Cited by 9 publications

References 37 publications

An unsupervised policy relevance scoring method: Taking Chinese social security policies as the application case

An unsupervised policy relevance scoring method: Taking Chinese social security policies as the application case

Extractive Arabic Text Summarization-Graph-Based Approach

The use of Semantic Role Labelling with Triangle-Graph Based Text Summarization

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