  Transformer-based models have consistently produced substantial performance
gains across a variety of NLP tasks, compared to shallow models. However, deep
models are orders of magnitude more computationally expensive than shallow
models, especially on tasks with large sequence lengths, such as document-level
event detection. In this work, we attempt to bridge the performance gap between
shallow and deep models on document-level event detection by using abstractive
text summarization as an augmentation method. We augment the DocEE dataset by
generating abstractive summaries of examples from low-resource classes. For
classification, we use linear SVM with TF-IDF representations and RoBERTa-base.
We use BART for zero-shot abstractive summarization, making our augmentation
setup less resource-intensive compared to supervised fine-tuning. We experiment
with four decoding methods for text generation, namely beam search, top-k
sampling, top-p sampling, and contrastive search. Furthermore, we investigate
the impact of using document titles as additional input for classification. Our
results show that using the document title offers 2.04% and 3.19% absolute
improvement in macro F1-score for linear SVM and RoBERTa, respectively.
Augmentation via summarization further improves the performance of linear SVM
by about 0.5%, varying slightly across decoding methods. Overall, our
augmentation setup yields insufficient improvements for linear SVM compared to
RoBERTa.
