We propose a novel framework for topic labeling that assigns the most representative phrases for a given set of sentences covering the same topic. We build an entailment graph over phrases that are extracted from the sentences, and use the entailment relations to identify and select the most relevant phrases. We then aggregate those selected phrases by means of phrase generalization and merging. We motivate our approach by applying over conversational data, and show that our framework improves performance significantly over baseline algorithms.

@inproceedings{mehdad-carenini-ng-joty-naacl-13, abstract = {We propose a novel framework for topic labeling that assigns the most representative phrases for a given set of sentences covering the same topic. We build an entailment graph over phrases that are extracted from the sentences, and use the entailment relations to identify and select the most relevant phrases. We then aggregate those selected phrases by means of phrase generalization and merging. We motivate our approach by applying over conversational data, and show that our framework improves performance significantly over baseline algorithms.}, address = {Atlanta, Georgia}, author = {Mehdad, Yashar and Carenini, Giuseppe and Ng, Raymond T. and Joty, Shafiq}, booktitle = {Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies}, month = {June}, pages = {179--189}, publisher = {ACL}, series = {NAACL'13}, title = {Towards Topic Labeling with Phrase Entailment and Aggregation}, year = {2013} }