  Article comprehension is an important challenge in natural language
processing with many applications such as article generation or
image-to-article retrieval. Prior work typically encodes all tokens in articles
uniformly using pretrained language models. However, in many applications, such
as understanding news stories, these articles are based on real-world events
and may reference many named entities that are difficult to accurately
recognize and predict by language models. To address this challenge, we propose
an ENtity-aware article GeneratIoN and rEtrieval (ENGINE) framework, to
explicitly incorporate named entities into language models. ENGINE has two main
components: a named-entity extraction module to extract named entities from
both metadata and embedded images associated with articles, and an entity-aware
mechanism that enhances the model's ability to recognize and predict entity
names. We conducted experiments on three public datasets: GoodNews, VisualNews,
and WikiText, where our results demonstrate that our model can boost both
article generation and article retrieval performance, with a 4-5 perplexity
improvement in article generation and a 3-4% boost in recall@1 in article
retrieval. We release our implementation at
https://github.com/Zhongping-Zhang/ENGINE .
