  We introduce PerSenT, a dataset of crowd-sourced annotations of the sentiment
expressed by the authors towards the main entities in news articles. The
dataset also includes paragraph-level sentiment annotations to provide more
fine-grained supervision for the task. Our benchmarks of multiple strong
baselines show that this is a difficult classification task. The results also
suggest that simply fine-tuning document-level representations from BERT isn't
adequate for this task. Making paragraph-level decisions and aggregating them
over the entire document is also ineffective. We present empirical and
qualitative analyses that illustrate the specific challenges posed by this
dataset. We release this dataset with 5.3k documents and 38k paragraphs
covering 3.2k unique entities as a challenge in entity sentiment analysis.
