This paper proposes a multilingual Automated Text Summarization (ATS) method targeting the Financial Narrative Summarization Task (FNS-2022). We developed two systems; the first uses a pre-trained abstractive summarization model that was fine-tuned on the downstream objective, the second approaches the problem as an extractive approach in which a similarity search is performed on the trained span representations. Both models aim to identify the beginning of the continuous narrative section of the document. The language models were fine-tuned on a financial document collection of three languages (English, Spanish, and Greek) and aim to identify the beginning of the summary narrative part of the document. The proposed systems achieve high performance in the given task, with the sequence-to-sequence variant ranked 1st on ROUGE-2 F1 score on the test set for each of the three languages.

Active Learning (AL) is a powerful tool for learning with less labeled data, in particular, for specialized domains, like legal documents, where unlabeled data is abundant, but the annotation requires domain expertise and is thus expensive. Recent works have shown the effectiveness of AL strategies for pre-trained language models. However, most AL strategies require a set of labeled samples to start with, which is expensive to acquire. In addition, pre-trained language models have been shown unstable during fine-tuning with small datasets, and their embeddings are not semantically meaningful. In this work, we propose a pipeline for effectively using active learning with pre-trained language models in the legal domain. To this end, we leverage the available unlabeled data in three phases. First, we continue pre-training the model to adapt it to the downstream task. Second, we use knowledge distillation to guide the model’s embeddings to a semantically meaningful space. Finally, we propose a simple, yet effective, strategy to find the initial set of labeled samples with fewer actions compared to existing methods. Our experiments on Contract-NLI, adapted to the classification task, and LEDGAR benchmarks show that our approach outperforms standard AL strategies, and is more efficient. Furthermore, our pipeline reaches comparable results to the fully-supervised approach with a small performance gap, and dramatically reduced annotation cost. Code and the adapted data will be made available.