mani1998machine Machine learning of generic and user-focused summarization

Mani, I. & Bloedorn, E. Machine learning of generic and user-focused summarization PROCEEDINGS OF THE NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, 1998, 821-826

Goal:
learn rules that easily edited by human
both generic and user-focused summarization
query in user-focused is a user abstract
does nor require manual tagging on training data
summary:
   generic: author abstract
   user-focused: automatically generated user "abstract". User choose relevant texts, then automatically choose some important words to choose sentences  as queries.


Introduction
Overall Approach
Features: locational, statistical, proper name, synonym
Traning Data: 198 text, 4-10 pages each. Compression rate 5% different rate
Learning Methods: 3 learning methods
 - Standarized Canonical Discriminant Function (SCDF) - SPSS 97. A multiple regression technique
 - C4.5 rules
 - AQ15C

Evaluation metric: accuracy, recall, precision, f-measure

Result
The best generic F: 0.69, user focused F: 0.89