  Document summarization provides an instrument for faster understanding the
collection of text documents and has several real-life applications. With the
growth of online text data, numerous summarization models have been proposed
recently. The Sequence-to-Sequence (Seq2Seq) based neural summarization model
is the most widely used in the summarization field due to its high performance.
This is because semantic information and structure information in the text is
adequately considered when encoding. However, the existing extractive
summarization models pay little attention to and use the central topic
information to assist the generation of summaries, which leads to models not
ensuring the generated summary under the primary topic. A lengthy document can
span several topics, and a single summary cannot do justice to all the topics.
Therefore, the key to generating a high-quality summary is determining the
central topic and building a summary based on it, especially for a long
document. We propose a topic-aware encoding for document summarization to deal
with this issue. This model effectively combines syntactic-level and
topic-level information to build a comprehensive sentence representation.
Specifically, a neural topic model is added in the neural-based sentence-level
representation learning to adequately consider the central topic information
for capturing the critical content in the original document. The experimental
results on three public datasets show that our model outperforms the
state-of-the-art models.
