Sumdoc: A Unified Approach for Automatic Text Summarization

Mohd, Mudasir; Shah, Muzaffar Bashir; Bhat, Shabir Ahmad; Kawa, Ummer Bashir; Khanday, Hilal Ahmad; Wani, Abid Hussain; Wani, Mohsin Altaf; Hashmy, Rana

doi:10.1007/978-981-10-0448-3_27

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…The unsupervised technique employed in the literature includes the works like [10][11][12]. They use different features to rank sentences and produce extractive summaries.…”

Section: Unsupervised Techniquesmentioning

confidence: 99%

Biomedical semantic text summarizer

Kirmani,

Kour,

Mohd

et al. 2024

BMC Bioinformatics

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Background Text summarization is a challenging problem in Natural Language Processing, which involves condensing the content of textual documents without losing their overall meaning and information content, In the domain of bio-medical research, summaries are critical for efficient data analysis and information retrieval. While several bio-medical text summarizers exist in the literature, they often miss out on an essential text aspect: text semantics. Results This paper proposes a novel extractive summarizer that preserves text semantics by utilizing bio-semantic models. We evaluate our approach using ROUGE on a standard dataset and compare it with three state-of-the-art summarizers. Our results show that our approach outperforms existing summarizers. Conclusion The usage of semantics can improve summarizer performance and lead to better summaries. Our summarizer has the potential to aid in efficient data analysis and information retrieval in the field of biomedical research.

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“…The unsupervised technique employed in the literature includes the works like [10][11][12]. They use different features to rank sentences and produce extractive summaries.…”

Section: Unsupervised Techniquesmentioning

confidence: 99%