@InProceedings{putra-tokunaga:2017:TextGraphs-11,
  author    = {Putra, Jan Wira Gotama  and  Tokunaga, Takenobu},
  title     = {Evaluating text coherence based on semantic similarity graph},
  booktitle = {Proceedings of TextGraphs-11: the Workshop on Graph-based Methods for Natural Language Processing},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {76--85},
  abstract  = {Coherence is a crucial feature of text because
	it is indispensable for conveying its
	communication purpose and meaning to
	its readers. In this paper, we propose an
	unsupervised text coherence scoring based
	on graph construction in which edges are
	established between semantically similar
	sentences represented by vertices. The
	sentence similarity is calculated based on
	the cosine similarity of semantic vectors
	representing sentences. We provide three
	graph construction methods establishing
	an edge from a given vertex to a preceding
	adjacent vertex, to a single similar
	vertex, or to multiple similar vertices.
	We evaluated our methods in the document
	discrimination task and the insertion
	task by comparing our proposed methods
	to the supervised (Entity Grid) and unsupervised
	(Entity Graph) baselines. In the
	document discrimination task, our method
	outperformed the unsupervised baseline
	but could not do the supervised baseline,
	while in the insertion task, our method outperformed
	both baselines.},
  url       = {http://www.aclweb.org/anthology/W17-2410}
}