Statistical Acquisition of Content Selection Rules for Natural Language Generation

Statistical Acquisition of Content Selection Rules for Natural Language Generation Pablo A. Duboue and Kathleen R. McKeown Department of Computer Science Columbia University � pablo,kathy � @cs.columbia.edu Abstract A Natural Language Generation system produces text using as input semantic data. One of its very first tasks is to decide which pieces of information to convey in the output. This task, called Content Se- lection, is quite domain dependent, requir- ing considerable re-engineering to trans- port the system from one scenario to an- other. In this paper, we present a method to acquire content selection rules automat- ically from a corpus of text and associated semantics. Our proposed technique was evaluated by comparing its output with in- formation selected by human authors in unseen texts, where we were able to fil- ter half the input data set without loss of recall. 1 Introduction CONTENT SELECTION is the task of choosing the right information to communicate in the output of a Natural Language Generation (NLG) system, given semantic input and a communicative goal. In gen- eral, Content Selection is a highly domain dependent task; new rules must be developed for each new do- main, and typically this is done manually. Morevoer, it has been argued (Sripada et al., 2001) that Content Selection is the most important task from a user’s standpoint (i.e., users may tolerate errors in wording, as long as the information being sought is present in the text). Designing content selection rules manually is a tedious task. A realistic knowledge base contains a large amount of information that could potentially be included in a text and a designer must examine a sizable number of texts, produced in different sit- uations, to determine the specific constraints for the selection of each piece of information. Our goal is to develop a system that can auto- matically acquire constraints for the content selec- tion task. Our algorithm uses the information we learned from a corpus of desired outputs for the sys- tem (i.e., human-produced text) aligned against re- lated semantic data (i.e., the type of data the sys- tem will use as input). It produces constraints on every piece of the input where constraints dictate if it should appear in the output at all and if so, under what conditions. This process provides a filter on the information to be included in a text, identifying all information that is potentially relevant (previously termed global focus (McKeown, 1985) or viewpoints (Acker and Porter, 1994)). The resulting informa- tion can be later either further filtered, ordered and augmented by later stages in the generation pipeline (e.g., see the spreading activation algorithm used in ILEX (Cox et al., 1999)). We focus on descriptive texts which realize a sin- gle, purely informative, communicative goal, as op- posed to cases where more knowledge about speaker intentions are needed. In particular, we present ex- periments on biographical descriptions, where the planned system will generate short paragraph length texts summarizing important facts about famous people. The kind of text that …

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