  Causality represents the foremost relation between events in financial
documents such as financial news articles, financial reports. Each financial
causality contains a cause span and an effect span. Previous works proposed
sequence labeling approaches to solve this task. But sequence labeling models
find it difficult to extract multiple causalities and overlapping causalities
from the text segments. In this paper, we explore a generative approach for
causality extraction using the encoder-decoder framework and pointer networks.
We use a causality dataset from the financial domain, \textit{FinCausal}, for
our experiments and our proposed framework achieves very competitive
performance on this dataset.
