Model Selection in Summary Evaluation
Abstract
A difficulty in the design of automated text summarization algorithms is in the objective evaluation. Viewing summarization as a tradeoff between length and information content, we introduce a technique based on a hierarchy of classifers to rank, through model selection, different summarization methods. This summary evaluation technique allows for broader comparison of summarization methods than the traditional techniques of summary evaluation. We present an empirical study of two simple, albeit widely used, summarization methods that shows the different usages of this automated task-based evaluation system and confirms the results obtained with human-based evaluation methods over smaller corpora.
Document Details
- Document Type
- Technical Report
- Publication Date
- Dec 01, 2002
- Accession Number
- ADA459486
Entities
People
- Luis Perez-breza
- Osamu Yoshimi
Organizations
- Massachusetts Institute of Technology