An Automatic Linguistics Approach for Persian Document Summarization

Kamyar, Hossein; Kahani, Mohsen; Kamyar, Mohsen; Poormasoomi, Asef

doi:10.1109/ialp.2011.52

Cited by 3 publications

(4 citation statements)

References 6 publications

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“…Kamyar et al (23) highlighted the significance of weighting words, a crucial step in language processing that influences the precision of text classification. They also presented a novel approach to weighting words, building upon the statistical TF-IDF weighting method.…”

Section: Literature Review and Research Backgroundmentioning

confidence: 99%

Accelerating Legislation Processes through Semantic Similarity Analysis with BERT-based Deep Learning

Naseri,

Hasanpour,

Ghanbari Sorkhi

2024

IJE

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Countries are managed based on accurate and precise laws. Enacting appropriate and timely laws can cause national progress. Each law is a textual term that is added to the set of existing laws after passing a process with the approval of the assembly. In the review of each new law, the relevant laws are extracted and analyzed among the set of existing laws. This paper presents a new solution for extracting the relevant rules for a term from an existing set of rules using semantic similarity and deep learning techniques based on the BERT model. The proposed method encodes sentences or paragraphs of text in a fixed-length vector (dense vector space). Thereafter, the vectors are utilized to evaluate and score the semantic similarity of the sentences with the cosine distance measurement scale. In the proposed method, the machine can understand the meaning and concept of the sentences by using the BERT model coding method. The BERT model considers the position of the entities in the sentences. Then the semantic similarities of documents, calculating the degree of similarity between their documents with a subject, and detecting their semantic similarity are done. The results obtained from the test dataset indicated the precision and accuracy of the method in detecting semantic similarities of legal documents related to the Islamic Consultative Assembly of Iran, as well as the precision and accuracy of performance above 90%.

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Section: Literature Review and Research Backgroundmentioning

confidence: 99%

Accelerating Legislation Processes through Semantic Similarity Analysis with BERT-based Deep Learning

Naseri,

Hasanpour,

Ghanbari Sorkhi

2024

IJE

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“…In rhetorical approach based text summarization, creates a rhetorical relationships between different parts of the text and concept text are decomposed and extracted as summary is based on the rhetoric. The structure of approach presents connections and interpretations between different sections and subsections of the text logically and then, the summary is formed from the rhetorical connections between the identified text units [11], [12].…”

Section: Rhetorical-approachmentioning

confidence: 99%

A brief review on text summarization methods

Rautray¹,

Swain²,

Dash³

et al. 2018

IJET

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In present scenario, text summarization is a popular and active field of research in both the Information Retrieval (IR) and Natural Language Processing (NLP) communities. Summarization is important for IR since it is a means to identify useful information by condensing the document from large corpus of data in an efficient way. In this study, different aspects of text summarization methods with strength, limitation and gap within the methods are presented.

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“…It identifies the main character of the story from the predicate argument owned by each entity, then chooses sentences that are related to the main character. Using centering theory, a concept for anaphora resolution, [6] uses this concept to choose highly related sentences based on semantic transition between each adjacent sentence pair. Another researcher [11] uses this concept to identify a pattern of semantic transitions in the summary by training onhuman-annotated summaries.…”

Section: A Extractive Summarizationmentioning

confidence: 99%

“…In extractive summarization, there are two main considerations for building a summary, namely how to choose the most important information appearing in the source documents [4,5]and how to present the chosen information in a coherent manner in the resulting summary [6,7]. The former has received much attention in previous research, using both statistical and/or linguistic approaches.…”

Section: Introductionmentioning

confidence: 99%

Improving coherence by reordering the output of extractive summarization using Centering Theory through genetic algorithm

Yuliawati

Manurung

2013

2013 International Conference on Advanced Computer Science and Information Systems (ICACSIS)

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Extractive summarization is a widely studied and fairly easy to implement technique. It works by choosing the most important parts of a document(s) as a summary. However, this can lead to a lack of coherence in the summary itself. In this study, the principle of continuity in Centering Theory is used to maintain the entity coherence between subsequent sentences obtained from extractive news summarizer. Simultaneously, the relative order of sentences belonging to the same source document is maintained. These two considerations are implemented as fitness functions for a genetic algorithm that is used to obtain the optimal ordering of sentences in the summary. Based on the results of our study involving human judgment, a weighted fitness function combining 75% continuity and 25% relative order yields the most acceptable sentence ordering.

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An Automatic Linguistics Approach for Persian Document Summarization

Cited by 3 publications

References 6 publications

Accelerating Legislation Processes through Semantic Similarity Analysis with BERT-based Deep Learning

Accelerating Legislation Processes through Semantic Similarity Analysis with BERT-based Deep Learning

A brief review on text summarization methods

Improving coherence by reordering the output of extractive summarization using Centering Theory through genetic algorithm

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