  A quality abstractive summary should not only copy salient source texts as
summaries but should also tend to generate new conceptual words to express
concrete details. Inspired by the popular pointer generator
sequence-to-sequence model, this paper presents a concept pointer network for
improving these aspects of abstractive summarization. The network leverages
knowledge-based, context-aware conceptualizations to derive an extended set of
candidate concepts. The model then points to the most appropriate choice using
both the concept set and original source text. This joint approach generates
abstractive summaries with higher-level semantic concepts. The training model
is also optimized in a way that adapts to different data, which is based on a
novel method of distantly-supervised learning guided by reference summaries and
testing set. Overall, the proposed approach provides statistically significant
improvements over several state-of-the-art models on both the DUC-2004 and
Gigaword datasets. A human evaluation of the model's abstractive abilities also
supports the quality of the summaries produced within this framework.
