Properties of corpora, such as the diversity of vocabulary and how tightly related texts cluster together, impact the best way to cluster short texts.We examine several such properties in a variety of corpora and track their effects on various combinations of similarity metrics and clustering algorithms.We show that semantic similarity metrics outperform traditional n-gram and dependency similarity metrics for kmeans clustering of a linguistically creative dataset, but do not help with less creative texts.Yet the choice of similarity metric interacts with the choice of clustering method.We find that graphbased clustering methods perform well on tightly clustered data but poorly on loosely clustered data.Semantic similarity metrics generate loosely clustered output even when applied to a tightly clustered dataset.Thus, the best performing clustering systems could not use semantic metrics.

We investigate the task of generating coherent survey articles for scientific topics. We introduce an extractive summarization algorithm that combines a content model with a discourse model to generate coherent and readable summaries of scientific topics using text from scientific articles relevant to the topic. Human evaluation on 15 topics in computational linguistics shows that our system produces significantly more coherent summaries than previous systems. Specifically, our system improves the ratings for coherence by 36% in human evaluation compared to C-Lexrank, a state of the art system for scientific article summarization.

Rahul Jha, Catherine Finegan-Dollak, Ben King, Reed Coke, Dragomir Radev. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2015.