ArticleBMC bioinformatics2013
Evaluating the use of different positional strategies for sentence selection in biomedical literature summarization.
Article in BMC bioinformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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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.
Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Text summarization in the biomedical domain: a systematic review of recent research.Journal of biomedical informatics · 2014Pooled it
- Automatically Summarizing Evidence from Clinical Trials: A Prototype Highlighting Current Challenges.Proceedings of the conference. Association for Computational Linguistics. Meeting · 2023Article
- CERC: an interactive content extraction, recognition, and construction tool for clinical and biomedical text.BMC medical informatics and decision making · 2020Article
- A manual corpus of annotated main findings of clinical case reports.Database : the journal of biological databases and curation · 2019Article
- Classification of clinically useful sentences in clinical evidence resources.Journal of biomedical informatics · 2016Article
- Feature engineering for MEDLINE citation categorization with MeSH.BMC bioinformatics · 2015Article
- Figure-associated text summarization and evaluation.PloS one · 2015Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe position of a sentence in a document has been traditionally considered an indicator of the relevance of the sentence, and therefore it is frequently used by automatic summarization systems as an attribute for sentence selection. Sentences close to the beginning of the document are supposed to deal with the main topic and thus are selected for the summary. This criterion has shown to be very effective when summarizing some types of documents, such as news items. However, this property is not likely to be found in other types of documents, such as scientific articles, where other positional criteria may be preferred. The purpose of the present work is to study the utility of different positional strategies for biomedical literature summarization.
resultsWe have evaluated three different positional strategies: (1) awarding the sentences at the beginning of the document, (2) preferring those at the beginning and end of the document, and (3) weighting the sentences according to the section in which they appear. To this end, we have implemented two summarizers, one based on semantic graphs and the other based on concept frequencies, and evaluated the summaries they produce when combined with each of the positional strategies above using ROUGE metrics. Our results indicate that it is possible to improve the quality of the summaries by weighting the sentences according to the section in which they appear (≈17% improvement in ROUGE-2 for the graph-based summarizer and ≈20% for the frequency-based summarizer), and that the sections containing the more salient information are the Methods and Material and the Discussion and Results ones.
conclusionsIt has been found that the use of traditional positional criteria that award sentences at the beginning and/or the end of the document are not helpful when summarizing scientific literature. In contrast, a more appropriate strategy is that which weights sentences according to the section in which they appear.
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Registered trials
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.