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Distributed document representation is one of the basic problems in naturallanguage processing. Currently distributed document representation methodsmainly consider the context information of words or sentences. These methods donot take into account the coherence of the document as a whole, e.g., arelation between the paper title and abstract, headline and description, oradjacent bodies in the document. The coherence shows whether a document ismeaningful, both logically and syntactically, especially in scientificdocuments (papers or patents, etc.). In this paper, we propose a coupled textpair embedding (CTPE) model to learn the representation of scientificdocuments, which maintains the coherence of the document with coupled textpairs formed by segmenting the document. First, we divide the document into twoparts (e.g., title and abstract, etc) which construct a coupled text pair.Then, we adopt negative sampling to construct uncoupled text pairs whose twoparts are from different documents. Finally, we train the model to judgewhether the text pair is coupled or uncoupled and use the obtained embedding ofcoupled text pairs as the embedding of documents. We perform experiments onthree datasets for one information retrieval task and two recommendation tasks.The experimental results verify the effectiveness of the proposed CTPE model.

Coherence-Based Distributed Document Representation Learning for Scientific Documents