Biomedical semantic text summarizer

Abstract

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.

Automatic summarization model based on clustering algorithm

“…Although these methods are effective, they mostly did not consider the semantic problem. To alleviate the semantic problem, Mohd et al 7 used a distributional semantic to capture and preserve the semantics of text as the fundamental feature for summarizing; Kirmani et al 8 utilized bio-semantic models on the domain of bio-medical research; Bhat et al 9 used emotion described by text as semantic feature; Kirmani et al 10 proposed an email summarizing system by semantic models and deep-learning technologies to summarize emails; Mud et al 11 proposed an advanced text document summarizer with cluster algorithm to preserving the underlying semantics of the original text. Although the above methods take into account the semantics of preserving the original text, it does not take into account the problem of redundancy, which may make the sentence semantic redundancy in the extracted summary.…”