Abstracts produced using computer assistance

Abstract

Experimental subjects wrote abstracts of articles using a simplified version of the TEXNET abstracting assistance software. In addition to the full text, subjects were presented with either keywords or phrases extracted automatically. The resulting abstracts, and the times taken, were recorded automatically; some additional information was gathered by oral questionnaire. Selected abstracts produced were evaluated on various criteria by independent raters. Results showed considerable variation among subjects, but 37% found the keywords or phrases "quite" or "very" useful in writing their abstracts. Statistical analysis failed to support several hypothesized relations: phrases were not viewed as significantly more helpful than keywords; and abstracting experience did not correlate with originality of wording, approximation of the author abstract, or greater conciseness. Requiring further study are some unanticipated strong correlations including the following: Windows experience and writing an abstract like the author's; experience reading abstracts and thinking one had written a good abstract; gender and abstract length; gender and use of words and phrases from the original text. Results have also suggested possible modifications to the TEXNET software.

Natural language processing

“…Building robust and domain‐independent systems is a complex and resource‐intensive task. Arguing that purely automatic abstracting systems do not always produce useful results, Craven (1988, 1993, 2000) proposes a hybrid abstracting system in which some tasks are performed by human abstractors and others by assistance software called TEXNET. However, recent experiments on the usefulness of the automatically extracted keywords and phrases in actual abstracting by human abstractors showed considerable variation among subjects, with only 37 percent of the abstractors finding keywords and phrases useful in writing their abstracts (Craven, 2000).…”

Using human authored description logics ABoxes as concept models for natural language generation

“…The work presented here aims to provide a clear form of “instant gratification” from creating semantic statements. Adopting a hybrid approach that integrates natural language processing techniques to assist human authors in creating semantic statements might be an effective way to ease the burden on the researcher (Craven 2000, Rzhetsky et al 2008).…”

Joint Optimization of User-desired Content in Multi-document Summaries by Learning from User Feedback

“…The majority of the existing computer-assisted summarization tools (Craven, 2000;Narita et al, 2002;Orǎsan et al, 2003;Orǎsan and Hasler, 2006) present important elements of a document to the user. Creating a summary then requires the human to cut, paste, and reorganize the important elements in order to formulate a final text.…”