A Formal Model for Information Selection in Multi-Sentence Text Extraction

Selecting important information while accounting for repetitions is a hard task for both summarization and question answering. We propose a formal model that represents a collection of documents in a two-dimensional space of textual and conceptual units with an associated mapping between these two dimensions. This representation is then used to describe the task of selecting textual units for a summary or answer as a formal optimization task. We provide approximation algorithms and empirically validate the performance of the proposed model when used with two very different sets of features, words and atomic events.