  A major proportion of a text summary includes important entities found in the
original text. These entities build up the topic of the summary. Moreover, they
hold commonsense information once they are linked to a knowledge base. Based on
these observations, this paper investigates the usage of linked entities to
guide the decoder of a neural text summarizer to generate concise and better
summaries. To this end, we leverage on an off-the-shelf entity linking system
(ELS) to extract linked entities and propose Entity2Topic (E2T), a module
easily attachable to a sequence-to-sequence model that transforms a list of
entities into a vector representation of the topic of the summary. Current
available ELS's are still not sufficiently effective, possibly introducing
unresolved ambiguities and irrelevant entities. We resolve the imperfections of
the ELS by (a) encoding entities with selective disambiguation, and (b) pooling
entity vectors using firm attention. By applying E2T to a simple
sequence-to-sequence model with attention mechanism as base model, we see
significant improvements of the performance in the Gigaword (sentence to title)
and CNN (long document to multi-sentence highlights) summarization datasets by
at least 2 ROUGE points.
