  Text Summarization is recognised as one of the NLP downstream tasks and it
has been extensively investigated in recent years. It can assist people with
perceiving the information rapidly from the Internet, including news articles,
social posts, videos, etc. Most existing research works attempt to develop
summarization models to produce a better output. However, advent limitations of
most existing models emerge, including unfaithfulness and factual errors. In
this paper, we propose a novel model, named as Knowledge-aware Abstractive Text
Summarization, which leverages the advantages offered by Knowledge Graph to
enhance the standard Seq2Seq model. On top of that, the Knowledge Graph
triplets are extracted from the source text and utilised to provide keywords
with relational information, producing coherent and factually errorless
summaries. We conduct extensive experiments by using real-world data sets. The
results reveal that the proposed framework can effectively utilise the
information from Knowledge Graph and significantly reduce the factual errors in
the summary.
