Automatic Text Summarization for Hindi Using Real Coded Genetic Algorithm

Jain, Arti; Arora, Anuja; Morato, Jorge; Yadav, Divakar; Kumar, Kumar Vimal

doi:10.3390/app12136584

Cited by 20 publications

(11 citation statements)

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“…Although GA can be considered an old technique, it continues to attract attention. In recent research efforts, it has been successfully applied in text analysis, particularly for text feature selection [40], text clustering [41], and text summarization [42].…”

Section: Discussionmentioning

confidence: 99%

Generating Representative Phrase Sets for Text Entry Experiments by GA-Based Text Corpora Sampling

Ljubić

Salkanović

2023

Mathematics

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In the field of human–computer interaction (HCI), text entry methods can be evaluated through controlled user experiments or predictive modeling techniques. While the modeling approach requires a language model, the empirical approach necessitates representative text phrases for the experimental stimuli. In this context, finding a phrase set with the best language representativeness belongs to the class of optimization problems in which a solution is sought in a large search space. We propose a genetic algorithm (GA)-based method for extracting a target phrase set from the available text corpus, optimizing its language representativeness. Kullback–Leibler divergence is utilized to evaluate candidates, considering the digram probability distributions of both the source corpus and the target sample. The proposed method is highly customizable, outperforms typical random sampling, and exhibits language independence. The representative phrase sets generated by the proposed solution facilitate a more valid comparison of the results from different text entry studies. The open source implementation enables the easy customization of the GA-based sampling method, promotes its immediate utilization, and facilitates the reproducibility of this study. In addition, we provide heuristic guidelines for preparing the text entry experiments, which consider the experiment’s intended design and the phrase set to be generated with the proposed solution.

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Section: Discussionmentioning

confidence: 99%

Generating Representative Phrase Sets for Text Entry Experiments by GA-Based Text Corpora Sampling

Ljubić

Salkanović

2023

Mathematics

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“…The comparative analysis of the proposed methodology is described in Table 5. The proposed method is compared with existing methods like ATS [21] and TF [26] which contains the kaggle dataset. The evaluation indicator ROUGE 1 and 2 is utilized for the comparison, the proposed method shows 1.83 in ROUGE-1 and 0.74 in ROUGE-2 which is comparatively higher than existing methods.…”

Section: Comparative Analysismentioning

confidence: 99%

“…Table 5. Represents the comparative analysis Methods Dataset ROUGE-1 ROUGE-2 Jain et al [21] Kaggle [23] 0.79 0.66 Sethi et al [22] 1.5 -Proposed method 1.83 0.74…”

Section: Comparative Analysismentioning

confidence: 99%

“…The drawback was the lack of personalization, the NLP-related suggestion model was usually based on common patterns, which could result in generic recommendations that do not align with particular preferences. Jain et al [21] implemented an ATS for the Hindi language by utilizing real coded genetic algorithm (RCGA). The rigorous experimentation on various feature groups was evaluated for differentiating features such as named sentence similarity and named entity features were merged with others for computing the evaluation metrics.…”

Section: Introductionmentioning

confidence: 99%

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Bidirectional recommendation in HR analytics through text summarization

Arandi,

Yeresime

2024

Bulletin EEI

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For over a decade, online job portals have been providing their services to both job seekers and employers in search of hiring opportunities. Because of the high demand for recruitment, it is insufficient to use conventional hiring methods to find a suitable candidate to fill the position. Validating resumes online is challenging due to the potential for manual errors, making the process inherently risky. The bidirectional method comprises named entity recognition (NER) for extracting the required resumes for recruiters. Cosine similarity shows the match percentage of resumes for the job requirements and vice versa. In an attempt to tackle an issue of unregistered words, a solution called decoder attention with pointer network (DA-PN) has been introduced. This method incorporates the use of coverage mechanism to prevent word repetition through generated text summary. DA-PN+Cover method with mixed learning objective (MLO) (DA-PN+Cover+MLO) is utilized for protecting grow of increasing faults in generated text summary. Performance of proposed method is estimated using evaluation indicator recall oriented understudy for gisting evaluation (ROUGE) and attains an average of 27.47 which is comparatively higher than existing methods.

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“…With their mathematical combination of nine textual feature-based methods and achieved 0.40 ROUGE score. (Jain et al, 2022) proposed Hindi text summarization using a real-coded genetic algorithm. After preprocessing the data, they extracted eight features from the text.…”

Section: Extractive Hindi Text Summarizationmentioning

confidence: 99%

Hindi Abstractive Text Summarization using Transliteration with Pre-trained Model

Shashi Shekhar, Rashmi Gupta

2024

jes

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Automatic text summarization is a subarea of natural language processing that generates a summary of the text by keeping its key points. The research work done on summarizing low-resourced language text is very limited. In India, the Hindi language is being spoken by central and north Indian people and only a few research works have been done on abstractive summarization of Hindi language. Having matras in Hindi makes it difficult to tokenize so it is difficult to summarize Hindi text using abstractive text summarization. In the proposed method, abstractive Hindi text summarization is done using transliteration and fine-tuning. In this work, the model is trained to generate both summaries and headlines. ROUGE-score and BERT-score have been utilized to check summary quality. A new semantic similarity score-based performance measure is also proposed to measure semantic similarity between reference summaries and predicted summaries. Using the proposed method, we have achieved the highest 55.16 ROUGE score, 0.80 BERT score, and 0.98 similarity score. Along with these performance measures, human evaluation of predicted summaries is also done and it is found that summaries and headlines were generated at a human-acceptable level.

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Automatic Text Summarization for Hindi Using Real Coded Genetic Algorithm

Cited by 20 publications

References 50 publications

Generating Representative Phrase Sets for Text Entry Experiments by GA-Based Text Corpora Sampling

Generating Representative Phrase Sets for Text Entry Experiments by GA-Based Text Corpora Sampling

Bidirectional recommendation in HR analytics through text summarization

Hindi Abstractive Text Summarization using Transliteration with Pre-trained Model

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