Evidence map›Paper›PMID 38627652›Full record

ArticleBMC bioinformatics2024

Biomedical semantic text summarizer.

Mahira Kirmani, Gagandeep Kour, Mudasir Mohd, Nasrullah Sheikh, Dawood Ashraf Khan, Zahid Maqbool, Mohsin Altaf Wani, Abid Hussain Wani

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Mahira KirmaniUniversity Institute of Computing, Chandigarh University, NH-05-Chandigarh-Ludhiana, Mohali, Punjab, India.
Gagandeep KourUniversity Institute of Computing, Chandigarh University, NH-05-Chandigarh-Ludhiana, Mohali, Punjab, India.
Mudasir MohdDepartment of Computer Science, University of Kashmir, South Campus, Anantnag, Jammu and Kashmir, India. mudasir.mohammad@kashmiruniversity.ac.in.
Nasrullah SheikhIBM Research, Almaden, 650 Harry Rd, San Jose, CA, 95120, USA.
Dawood Ashraf KhanThndr, The Office 3, One central, DWTC, Dubai, United Arab Emirates.
Zahid MaqboolDepartment of Computer Science, Government Degree College Bemina, Srinagar, Jammu and Kashmir, India.
Mohsin Altaf WaniDepartment of Computer Science, University of Kashmir, South Campus, Anantnag, Jammu and Kashmir, India.
Abid Hussain WaniDepartment of Computer Science, University of Kashmir, South Campus, Anantnag, Jammu and Kashmir, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundText 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.

resultsThis 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.

conclusionThe 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.

Indexed as

AlgorithmsBiomedical ResearchInformation Storage and RetrievalNatural Language ProcessingSemanticsBiomedical text summarizaionSemantic modelsText semanticsText summarization

Identifiers

PMID38627652
PMCPMC11022460

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.