Goh, A.; Hui, S.C.; Chan, S.K.: ¬A text extraction system for news reports (1996)
0.00
0.0022374375 = product of:
0.004474875 = sum of:
0.004474875 = product of:
0.00894975 = sum of:
0.00894975 = weight(_text_:a in 6601) [ClassicSimilarity], result of:
0.00894975 = score(doc=6601,freq=14.0), product of:
0.053105544 = queryWeight, product of:
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.046056706 = queryNorm
0.1685276 = fieldWeight in 6601, product of:
3.7416575 = tf(freq=14.0), with freq of:
14.0 = termFreq=14.0
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.0390625 = fieldNorm(doc=6601)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Abstract
- Describes the design and implementation of a text extraction tool, NEWS_EXT, which aztomatically produces summaries from news reports by extracting sentences to form indicative abstracts. Selection of sentences is based on sentence importance, measured by means of sentence scoring or simple linguistic analysis of sentence structure. Tests were conducted on 4 approaches for the functioning of the NEWS_EXT system; extraction by keyword frequency; extraction by title keywords; extraction by location; and extraction by indicative phrase. Reports results of a study to compare the results of the application of NEWS_EXT with manually produced extracts; using relevance as the criterion for effectiveness. 48 newspaper articles were assessed (The Straits Times, International Herald Tribune, Asian Wall Street Journal, and Financial Times). The evaluation was conducted in 2 stages: stage 1 involving abstracts produced manually by 2 human experts; stage 2 involving the generation of abstracts using NEWS_EXT. Results of each of the 4 approaches were compared with the human produced abstracts, where the title and location approaches were found to give the best results for both local and foreign news. Reports plans to refine and enhance NEWS_EXT and incorporate it as a module within a larger newspaper clipping system
- Type
- a
