Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Apr 16;25(1):152.
doi: 10.1186/s12859-024-05712-x.

Biomedical semantic text summarizer

Mahira Kirmani  1 , Gagandeep Kour  1 , Mudasir Mohd  2 , Nasrullah Sheikh  3 , Dawood Ashraf Khan  4 , Zahid Maqbool  5 , Mohsin Altaf Wani  6 , Abid Hussain Wani  6
Affiliations

Biomedical semantic text summarizer

Mahira Kirmani et al. BMC Bioinformatics. .

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.

Keywords: Biomedical text summarizaion; Semantic models; Text semantics; Text summarization.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Overall working of the system
Algorithm 1
Algorithm 1
Summarizing algorithm
Fig. 2
Fig. 2
Functioning of ranking algorithm
Fig. 3
Fig. 3
F-Score for 15% summary length
Fig. 4
Fig. 4
F-Score for 25% summary length
See this image and copyright information in PMC

References

    1. Moradi M, Ghadiri N. Different approaches for identifying important concepts in probabilistic biomedical text summarization. Artif Intell Med. 2018;84:101–116. doi: 10.1016/j.artmed.2017.11.004. - DOI - PubMed
    1. Kirmani M, Kaur G, Mohd M. ShortMail: an email summarizer system. Software Impacts. 2023;17:100543. doi: 10.1016/j.simpa.2023.100543. - DOI
    1. Mohd M, Wani MA, Khanday HA, Mir UB, Nasrullah S, Maqbool Z, et al. Semantic-summarizer: semantics-based text summarizer for English language text. Software Impacts. 2023;18:100582. doi: 10.1016/j.simpa.2023.100582. - DOI
    1. Mohd M, Jan R, Shah M. Text document summarization using word embedding. Expert Syst Appl. 2020;143:112958. doi: 10.1016/j.eswa.2019.112958. - DOI
    1. Givchi A, Ramezani R, Baraani-Dastjerdi A. Graph-based abstractive biomedical text summarization. J Biomed Inform. 2022;132:104099. doi: 10.1016/j.jbi.2022.104099. - DOI - PubMed

LinkOut - more resources