PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 16779153

Medical textbook summarization and guided navigation using statistical sentence extraction.

Gregory Whalen
View original source ↗Export metadata ↓

Abstract

We present a method for automated medical textbook and encyclopedia summarization. Using statistical sentence extraction and semantic relationships, we extract sentences from text returned as part of an existing textbook search (similar to a book index). Our system guides users to the information they desire by summarizing the content of each relevant chapter or section returned in the search. The summary is tailored to contain sentences that specifically address the user's search terms. Our clustering method selects sentences that contain concepts specifically addressing the context of the query term in each of the returned sections. Our method examines conceptual relationships from the UMLS and selects clusters of concepts using Expectation Maximization (EM). Sentences associated with the concept clusters are shown to the user. We evaluated whether our extracted summary provides a suitable answer to the user's question.

Explore related subjects

Abstracting and IndexingAlgorithmsEncyclopedias as TopicInformation Storage and RetrievalNatural Language ProcessingOnline SystemsStatistics as TopicTextbooks as TopicUnified Medical Language SystemJournal ArticleResearch Support, U.S. Gov't, Non-P.H.S.

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Gregory Whalen. 2005. Medical textbook summarization and guided navigation using statistical sentence extraction.. https://pubmed.ncbi.nlm.nih.gov/16779153/

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2007

A comparative evaluation of full-text, concept-based, and context-sensitive search.

OBJECTIVES: Study comparatively (1) concept-based search, using documents pre-indexed by a conceptual hierarchy; (2) context-sensitive search, using structured, labeled documents; and (3) traditional full-text search. Hypotheses were: (1) more contexts lead to better retrieval accuracy; and (2) adding concept-based search to the other searches would improve upon their baseline performances. DESIGN: Use our Vaidurya architecture, for search and retrieval evaluation, of structured documents classified by a conceptual hierarchy, on a clinical guidelines test collection. MEASUREMENTS: Precision computed at different levels of recall to assess the contribution of the retrieval methods. Comparisons of precisions done with recall set at 0.5, using t-tests. RESULTS: Performance increased monotonically with the number of query context elements. Adding context-sensitive elements, mean improvement was 11.1% at recall 0.5. With three contexts, mean query precision was 42% +/- 17% (95% confidence interval [CI], 31% to 53%); with two contexts, 32% +/- 13% (95% CI, 27% to 38%); and one context, 20% +/- 9% (95% CI, 15% to 24%). Adding context-based queries to full-text queries monotonically improved precision beyond the 0.4 level of recall. Mean improvement was 4.5% at recall 0.5. Adding concept-based search to full-text search improved precision to 19.4% at recall 0.5. CONCLUSIONS: The study demonstrated usefulness of concept-based and context-sensitive queries for enhancing the precision of retrieval from a digital library of semi-structured clinical guideline documents. Concept-based searches outperformed free-text queries, especially when baseline precision was low. In general, the more ontological elements used in the query, the greater the resulting precision.

Abstracting and Indexing↗
PubMed2007

Improvements to the Red List Index.

The Red List Index uses information from the IUCN Red List to track trends in the projected overall extinction risk of sets of species. It has been widely recognised as an important component of the suite of indicators needed to measure progress towards the international target of significantly reducing the rate of biodiversity loss by 2010. However, further application of the RLI (to non-avian taxa in particular) has revealed some shortcomings in the original formula and approach: It performs inappropriately when a value of zero is reached; RLI values are affected by the frequency of assessments; and newly evaluated species may introduce bias. Here we propose a revision to the formula, and recommend how it should be applied in order to overcome these shortcomings. Two additional advantages of the revisions are that assessment errors are not propagated through time, and the overall level extinction risk can be determined as well as trends in this over time.

Abstracting and Indexing↗