Abstract
Comprehensive document encoding and
salient information selection are two major
difficulties for generating summaries with
adequate salient information. To tackle the
above difficulties, we propose a Transformerbased encoder-decoder framework with two
novel extensions for abstractive document
summarization. Specifically, (1) to encode
the documents comprehensively, we design a
focus-attention mechanism and incorporate
it into the encoder. This mechanism models
a Gaussian focal bias on attention scores
to enhance the perception of local context,
which contributes to producing salient and
informative summaries. (2) To distinguish
salient information precisely, we design an
independent saliency-selection network which
manages the information flow from encoder
to decoder. This network effectively reduces
the influences of secondary information on the
generated summaries. Experimental results
on the popular CNN/Daily Mail benchmark
demonstrate that our model outperforms other
state-of-the-art baselines on the ROUGE
metrics