We present a new unsupervised method for learning general-purpose sentence embeddings.
 Unlike existing methods which rely on local contexts, such as words
 inside the sentence or immediately neighboring sentences, our method selects, for
 each target sentence, influential sentences in the entire document based on a document
 structure. We identify a dependency structure of sentences using metadata
 or text styles. Furthermore, we propose a novel out-of-vocabulary word handling
 technique to model many domain-specific terms, which were mostly discarded by
 existing sentence embedding methods. We validate our model on several tasks
 showing 30% precision improvement in coreference resolution in a technical domain,
 and 7.5% accuracy increase in paraphrase detection compared to baselines.
To train a sentence embedding using technical documents, our approach considers document structure to find broader context and handle out-of-vocabulary words. Presents ideas for improving sentence embedding by drawing from more context. Learning sentence representations with sentences dependencies information Extends the idea of forming an unsupervised representation of sentences used in the SkipThough approach by using a broader set of evidence for forming the representation of a sentence