Distributed document representation is one of the basic problems in natural
language processing. Currently distributed document representation methods
mainly consider the context information of words or sentences. These methods do
not take into account the coherence of the document as a whole, e.g., a
relation between the paper title and abstract, headline and description, or
adjacent bodies in the document. The coherence shows whether a document is
meaningful, both logically and syntactically, especially in scientific
documents (papers or patents, etc.). In this paper, we propose a coupled text
pair embedding (CTPE) model to learn the representation of scientific
documents, which maintains the coherence of the document with coupled text
pairs formed by segmenting the document. First, we divide the document into two
parts (e.g., title and abstract, etc) which construct a coupled text pair.
Then, we adopt negative sampling to construct uncoupled text pairs whose two
parts are from different documents. Finally, we train the model to judge
whether the text pair is coupled or uncoupled and use the obtained embedding of
coupled text pairs as the embedding of documents. We perform experiments on
three datasets for one information retrieval task and two recommendation tasks.
The experimental results verify the effectiveness of the proposed CTPE model.

本文提出一种耦合的文本对嵌入（CTPE）模型，用于学习科学文档的表示，以维护文档的连贯性，并通过实验证明其有效性。