Aggregation via Set Partitioning for Natural Language Generation

Proceedings of the Human Language Technology Conference of the North American Chapter of the ACL, pages 359–366, New York, June 2006. c⃝2006 Association for Computational Linguistics Aggregation via Set Partitioning for Natural Language Generation Regina Barzilay Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology regina@csail.mit.edu Mirella Lapata School of Informatics University of Edinburgh mlap@inf.ed.ac.uk Abstract The role of aggregation in natural lan- guage generation is to combine two or more linguistic structures into a single sentence. The task is crucial for generat- ing concise and readable texts. We present an efficient algorithm for automatically learning aggregation rules from a text and its related database. The algorithm treats aggregation as a set partitioning problem and uses a global inference procedure to find an optimal solution. Our experiments show that this approach yields substan- tial improvements over a clustering-based model which relies exclusively on local information. 1 Introduction Aggregation is an essential component of many nat- ural language generation systems (Reiter and Dale, 2000). The task captures a mechanism for merg- ing together two or more linguistic structures into a single sentence. Aggregated texts tend to be more concise, coherent, and more readable overall (Dalia- nis, 1999; Cheng and Mellish, 2000). Compare, for example, sentence (2) in Table 1 and its non- aggregated counterpart in sentences (1a)–(1d). The difference between the fluent aggregated sentence and its abrupt and redundant alternative is striking. The benefits of aggregation go beyond making texts less stilted and repetitive. Researchers in psy- cholinguistics have shown that by eliminating re- (1) a. Holocomb had an incompletion in the first quarter. b. Holocomb had another incompletion in the first quarter. c. Davis was among four San Francisco defenders. d. Holocomb threw to Davis for a leaping catch. (2) After two incompletions in the first quar- ter, Holcomb found Davis among four San Francisco defenders for a leaping catch. Table 1: Aggregation example (in boldface) from a corpus of football summaries dundancy, aggregation facilitates text comprehen- sion and recall (see Yeung (1999) and the references therein). Furthermore, Di Eugenio et al. (2005) demonstrate that aggregation can improve learning in the context of an intelligent tutoring application. In existing generation systems, aggregation typi- cally comprises two processes: semantic grouping and sentence structuring (Wilkinson, 1995). The first process involves partitioning semantic content (usually the output of a content selection compo- nent) into disjoint sets, each corresponding to a sin- gle sentence. The second process is concerned with syntactic or lexical decisions that affect the realiza- tion of an aggregated sentence. To date, this task has involved human analysis of a domain-relevant corpus and manual development of aggregation rules (Dalianis, 1999; Shaw, 1998). The corpus analysis and knowledge engineering work in such an approach is substantial, prohibitively so in 359 large domains. But since corpus data is already used in building aggregation components, an appealing alternative is to try and learn the rules of semantic grouping directly from the data. Clearly, this would greatly reduce the human effort involved and ease …

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