Moreno La Quatra


2023

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Transformer-based Prediction of Emotional Reactions to Online Social Network Posts
Irene Benedetto | Moreno La Quatra | Luca Cagliero | Luca Vassio | Martino Trevisan
Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis

Emotional reactions to Online Social Network posts have recently gained importance in the study of the online ecosystem. Prior to post publication, the number of received reactions can be predicted based on either the textual content of the post or the related metadata. However, existing approaches suffer from both the lack of semantic-aware language understanding models and the limited explainability of the prediction models. To overcome these issues, we present a new transformer-based method to predict the number of emotional reactions of different types to social posts. It leverages the attention mechanism to capture arbitrary semantic textual relations neglected by prior works. Furthermore, it also provides end-users with textual explanations of the predictions. The results achieved on a large collection of Facebook posts confirm the applicability of the presented methodology.

2020

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End-to-end Training For Financial Report Summarization
Moreno La Quatra | Luca Cagliero
Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation

Quoted companies are requested to periodically publish financial reports in textual form. The annual financial reports typically include detailed financial and business information, thus giving relevant insights into company outlooks. However, a manual exploration of these financial reports could be very time consuming since most of the available information can be deemed as non-informative or redundant by expert readers. Hence, an increasing research interest has been devoted to automatically extracting domain-specific summaries, which include only the most relevant information. This paper describes the SumTO system architecture, which addresses the Shared Task of the Financial Narrative Summarisation (FNS) 2020 contest. The main task objective is to automatically extract the most informative, domain-specific textual content from financial, English-written documents. The aim is to create a summary of each company report covering all the business-relevant key points. To address the above-mentioned goal, we propose an end-to-end training method relying on Deep NLP techniques. The idea behind the system is to exploit the syntactic overlap between input sentences and ground-truth summaries to fine-tune pre-trained BERT embedding models, thus making such models tailored to the specific context. The achieved results confirm the effectiveness of the proposed method, especially when the goal is to select relatively long text snippets.