Enhancing E-Recruitment Recommendations Through Text Summarization Techniques
This research aims to enhance e-recruitment systems using text summarization techniques and pretrained large language models (LLMs). A job recommender system is built with integrated text summarization. The text summarization techniques that are selected are BART, T5 (Text-to-Text Transfer Transform...
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MDPI AG
2025-04-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/16/4/333 |
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| author | Reham Hesham El-Deeb Walid Abdelmoez Nashwa El-Bendary |
| author_facet | Reham Hesham El-Deeb Walid Abdelmoez Nashwa El-Bendary |
| author_sort | Reham Hesham El-Deeb |
| collection | DOAJ |
| description | This research aims to enhance e-recruitment systems using text summarization techniques and pretrained large language models (LLMs). A job recommender system is built with integrated text summarization. The text summarization techniques that are selected are BART, T5 (Text-to-Text Transfer Transformer), BERT, and Pegasus. Content-based recommendation is the model chosen to be implemented. The LinkedIn Job Postings dataset is used. The evaluation of the text summarization techniques is performed using ROUGE-1, ROUGE-2, and ROUGE-L. The results of this approach deduce that the recommendation does improve after text summarization. BERT outperforms other summarization techniques. Recommendation evaluations show that, for MRR, BERT performs 256.44% better, indicating relevant recommendations at the top more effectively. For RMSE, there is a 29.29% boost, indicating recommendations closer to the actual values. For MAP, a 106.46% enhancement is achieved, presenting the highest precision in recommendations. Lastly, for NDCG, there is an 83.94% increase, signifying that the most relevant recommendations are ranked higher. |
| format | Article |
| id | doaj-art-2eb8824a954c488cbea7d77c3598fd32 |
| institution | DOAJ |
| issn | 2078-2489 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj-art-2eb8824a954c488cbea7d77c3598fd322025-08-20T03:13:32ZengMDPI AGInformation2078-24892025-04-0116433310.3390/info16040333Enhancing E-Recruitment Recommendations Through Text Summarization TechniquesReham Hesham El-Deeb0Walid Abdelmoez1Nashwa El-Bendary2College of Computing and Information Technology, Arab Academy for Science, Technology & Maritime Transport, Alexandria P.O. Box 1029, EgyptCollege of Computing and Information Technology, Arab Academy for Science, Technology & Maritime Transport, Alexandria P.O. Box 1029, EgyptCollege of Computing and Information Technology, Arab Academy for Science, Technology & Maritime Transport, Aswan P.O. Box 11, EgyptThis research aims to enhance e-recruitment systems using text summarization techniques and pretrained large language models (LLMs). A job recommender system is built with integrated text summarization. The text summarization techniques that are selected are BART, T5 (Text-to-Text Transfer Transformer), BERT, and Pegasus. Content-based recommendation is the model chosen to be implemented. The LinkedIn Job Postings dataset is used. The evaluation of the text summarization techniques is performed using ROUGE-1, ROUGE-2, and ROUGE-L. The results of this approach deduce that the recommendation does improve after text summarization. BERT outperforms other summarization techniques. Recommendation evaluations show that, for MRR, BERT performs 256.44% better, indicating relevant recommendations at the top more effectively. For RMSE, there is a 29.29% boost, indicating recommendations closer to the actual values. For MAP, a 106.46% enhancement is achieved, presenting the highest precision in recommendations. Lastly, for NDCG, there is an 83.94% increase, signifying that the most relevant recommendations are ranked higher.https://www.mdpi.com/2078-2489/16/4/333information retrievalrecommender systemsartificial intelligencenatural language processingpretrained large language modelstext summarization |
| spellingShingle | Reham Hesham El-Deeb Walid Abdelmoez Nashwa El-Bendary Enhancing E-Recruitment Recommendations Through Text Summarization Techniques Information information retrieval recommender systems artificial intelligence natural language processing pretrained large language models text summarization |
| title | Enhancing E-Recruitment Recommendations Through Text Summarization Techniques |
| title_full | Enhancing E-Recruitment Recommendations Through Text Summarization Techniques |
| title_fullStr | Enhancing E-Recruitment Recommendations Through Text Summarization Techniques |
| title_full_unstemmed | Enhancing E-Recruitment Recommendations Through Text Summarization Techniques |
| title_short | Enhancing E-Recruitment Recommendations Through Text Summarization Techniques |
| title_sort | enhancing e recruitment recommendations through text summarization techniques |
| topic | information retrieval recommender systems artificial intelligence natural language processing pretrained large language models text summarization |
| url | https://www.mdpi.com/2078-2489/16/4/333 |
| work_keys_str_mv | AT rehamheshameldeeb enhancingerecruitmentrecommendationsthroughtextsummarizationtechniques AT walidabdelmoez enhancingerecruitmentrecommendationsthroughtextsummarizationtechniques AT nashwaelbendary enhancingerecruitmentrecommendationsthroughtextsummarizationtechniques |