Silvia Casola


2022

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Exploring the limits of a base BART for multi-document summarization in the medical domain
Ishmael Obonyo | Silvia Casola | Horacio Saggion
Proceedings of the Third Workshop on Scholarly Document Processing

This paper is a description of our participation in the Multi-document Summarization for Literature Review (MSLR) Shared Task, in which we explore summarization models to create an automatic review of scientific results. Rather than maximizing the metrics using expensive computational models, we placed ourselves in a situation of scarce computational resources and explore the limits of a base sequence to sequence models (thus with a limited input length) to the task. Although we explore methods to feed the abstractive model with salient sentences only (using a first extractive step), we find the results still need some improvements.

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What’s in a (dataset’s) name? The case of BigPatent
Silvia Casola | Alberto Lavelli | Horacio Saggion
Proceedings of the 2nd Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)

Sharing datasets and benchmarks has been crucial for rapidly improving Natural Language Processing models and systems. Documenting datasets’ characteristics (and any modification introduced over time) is equally important to avoid confusion and make comparisons reliable. Here, we describe the case of BigPatent, a dataset for patent summarization that exists in at least two rather different versions under the same name. While previous literature has not clearly distinguished among versions, their differences do not only lay on a surface level but also modify the dataset’s core nature and, thus, the complexity of the summarization task. While this paper describes a specific case, we aim to shed light on new challenges that might emerge in resource sharing and advocate for comprehensive documentation of datasets and models.

2020

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FBK@SMM4H2020: RoBERTa for Detecting Medications on Twitter
Silvia Casola | Alberto Lavelli
Proceedings of the Fifth Social Media Mining for Health Applications Workshop & Shared Task

This paper describes a classifier for tweets that mention medications or supplements, based on a pretrained transformer. We developed such a system for our participation in Subtask 1 of the Social Media Mining for Health Application workshop, which featured an extremely unbalanced dataset. The model showed promising results, with an F1 of 0.8 (task mean: 0.66).