A Malay text summarizer using pattern-growth method with sentence compression rules

Alias, Suraya; Mohammad, Siti Khaotijah; Gan, Keng Hoon; Ping, Tan Tien

doi:10.1109/infrkm.2016.7806326

Cited by 8 publications

(5 citation statements)

References 25 publications

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“…For These unique features are extracted from the process of text summarization of Fabricated Hadith or Shia Rate of Hadith with the size of 10 words, using the approach that suggested by Karim in [19] and references from Suraya Alias, et al in [20], but with additional modification with inclusion of Malay Stemmer. Malay Stop Words and Malay Root Word from the Ahmad F. in [21] and the researchers work in [22], in its process.…”

Section: Proposed Methodsmentioning

confidence: 99%

Domain specific concept ontologies and text summarization as hierarchical fuzzy logic ranking indicator on malay text corpus

Rodzman

Ismail

Rahman

et al. 2019

IJEECS

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<span>Ranking function is a predictive algorithm that is used to establish a simple ordering of documents according to its relevance. This step is critical because the results’ quality of a Domain Specific Information Retrieval (IR) such as Hadith Information Retrieval is fundamentally dependent of the ranking function. A Hierarchical Fuzzy Logic Controller of <em>Mamdani</em>-type Fuzzy Inference System has been built to define the ranking function, based on the Malay Information retrieval’s BM25 Model. The model examines three-inputs (Ontology BM25 Score, Fabrication Rate of Hadith and Shia Rate of Hadith) and four-output values of Final Ranking Score which consist of three triangular membership functions. The proposed system has outperformed the BM25 original score and the Vector Space Model (VM) on 16 queries, while the BM25 original score and Vector Space Model only yield better result in 9 and 2 queries respectively on the P@10, %no measures and MAP. P@10 represent the values of Precision at Rank 10 P@10), %no measures represent the percentage of queries with no relevant documents in the top ten retrieved and MAP represents Mean Average Precision of the queries. The results show the proposed system have capability to demote negative documents and move up the relevant documents in the ranking list and its capability to recall unseen document with the application of ontology in text retrieval. For the future works, the researcher would like to apply the usage of other Malay Semantic elements and another corpus for positive ranking indicator.</span>

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Section: Proposed Methodsmentioning

confidence: 99%

Domain specific concept ontologies and text summarization as hierarchical fuzzy logic ranking indicator on malay text corpus

Rodzman

Ismail

Rahman

et al. 2019

IJEECS

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“…Moreover, SoW is developed not only for appearance of word, but also appearance of phrase and sentence in the document. SoW had been proven can increase the text mining result (either in classification or clustering) [17], [40], information retrieval [37], [39], [77], and even in text summarization [78], [79]. It is an evidance that SoW can maintain the meaning of text well.…”

Section: Structured Representation For Textmentioning

confidence: 99%

“…Frequent sequence is used as maximal frequent sentences for extracting text summary [107], and sequential sentence for multi document summarization to find the similarities and correlation between text resources using sequential pattern to keep the meaning of semantic knowledge of sentence in the multiple document (with Malay language) [79]. For another Malay language, sequential is used as text representation for text summarizer uses FASP (Frequent Adjacent Sequential Pattern) and FASPe (Frequent Eliminated Pattern) to represent Malay text and it focused on readability of text summary [78], and multilingual text summarization research uses itemset-based model to summarize the collection of document begin from the same topic.…”

Section: Indonesianmentioning

confidence: 99%

“…in English and Malay[79], Frequent Pattern and TF-IDF[102], FASP (Frequent Adjacent Sequential Pattern) and FASPe (Frequent Eliminated Pattern) for Malay[78], MWI-Sum (Multilingual Weighted Itemsetbased Summarizer)[103], Frequent itemset sequence generation algorithm[104], Term-based and ontology-based methods[105] Closed Frequent Patterm and Genetic Algorithm[106], Term Weighting and Sentence Selection[107].…”

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Sequential Pattern Mining and Deep Learning to Enhance Readability of Indonesian Text Summarization

Maylawati¹

2019

IJATCSE

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Readability is a big challenge that must be solved in automatic text summarization research. The aim of this study is to comprehensively and systematically investigate the literatures that are related with text summarization needs, in particular, to prepare efficient algorithm for Indonesian text using Deep Learning and Sequential Pattern Mining. Evidence from previous literatures shows that there are several studies which use Sequential Pattern Mining to extract representation of text for document clustering and classification of Indonesian text. However, not much attention was given to text representation in document summarization, specifically for Indonesian language. As readability is a major concern in the text summarization community, determining a better text representation to maintain the meaning of the generated text summary is deemed necessary. This paper gives an opportunity to take a deeper look into how to design an efficient and effective text representation which can further enhance text summarization readability. Besides the general systematic literature review, we discuss an idea to combine Deep Learning and Sequential Pattern Mining to improve readability of summary result to develop in the future.

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“…While the SoW as text representation that maintains the meaning of the text and minimize the reader's understanding gap with the summary results, to reach readability of summary result. There is a text summarization study that uses the SoW for the Malaysian language [23], [24], which is one family with the Indonesian language, but there are still many differences. Therefore, this study aims to investigate and enhance the quality of Indonesian text summary result with SoW as text representation to maintain the meaning of the text.…”

Section: Introductionmentioning

confidence: 99%

Feature-based approach and sequential pattern mining to enhance quality of Indonesian automatic text summarization

Maylawati

Kumar

Kasmin

2023

IJEECS

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Indonesian automatic text summarization research is developed rapidly. The quality, especially readability aspect, of text summary can be reached if the meaning of the text can be maintained properly. Therefore, this research aims to enhance the quality of extractive Indonesian automatic text summarization with considering the quality of structured representation of text. This research uses sequential pattern mining (SPM) to produce This research use SPM to produce sequence of words (SoW) as structured text representation using PrefixSpan algorithm. Then, SPM is combined with feature-based approach using sentence scoring method to produce summary. The experiment result using IndoSum dataset shows that even though the combination of SPM and sentence scoring can increase the precision value of recall-oriented understudy for gisting evaluation (ROUGE)-1, ROUGE-2, and ROUGE-L, from 0.68 to 0.76, 0.54 to 0.69, and 0.51 to 0.72. Especially, combination of SPM and Sentence Scoring can enhance precision, recall, and f-measure of ROUGE-L that consider the order of word occurance in measurement. SPM increases ROUGE-L f-measure value of sentence scoring from 0.32 to 0.36. Moreover, combination of sentence scoring and SPM is better than SumBasic that used as feature-based approach in the previous Indonesian text summarization research.

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A Malay text summarizer using pattern-growth method with sentence compression rules

Cited by 8 publications

References 25 publications

Domain specific concept ontologies and text summarization as hierarchical fuzzy logic ranking indicator on malay text corpus

Domain specific concept ontologies and text summarization as hierarchical fuzzy logic ranking indicator on malay text corpus

Sequential Pattern Mining and Deep Learning to Enhance Readability of Indonesian Text Summarization

Feature-based approach and sequential pattern mining to enhance quality of Indonesian automatic text summarization

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