Proceedings of the 2nd Workshop on Deriving Insights from User-Generated Text

Estevam Hruschka, Tom Mitchell, Dunja Mladenic, Marko Grobelnik, Nikita Bhutani (Editors)

Anthology ID:
(Hybrid) Dublin, Ireland, and Virtual
Association for Computational Linguistics
Bib Export formats:

pdf bib
Proceedings of the 2nd Workshop on Deriving Insights from User-Generated Text
Estevam Hruschka | Tom Mitchell | Dunja Mladenic | Marko Grobelnik | Nikita Bhutani

pdf bib
Unsupervised Abstractive Dialogue Summarization with Word Graphs and POV Conversion
Seongmin Park | Jihwa Lee

We advance the state-of-the-art in unsupervised abstractive dialogue summarization by utilizing multi-sentence compression graphs. Starting from well-founded assumptions about word graphs, we present simple but reliable path-reranking and topic segmentation schemes. Robustness of our method is demonstrated on datasets across multiple domains, including meetings, interviews, movie scripts, and day-to-day conversations. We also identify possible avenues to augment our heuristic-based system with deep learning. We open-source our code, to provide a strong, reproducible baseline for future research into unsupervised dialogue summarization.

pdf bib
An Interactive Analysis of User-reported Long COVID Symptoms using Twitter Data
Lin Miao | Mark Last | Marina Litvak

With millions of documented recoveries from COVID-19 worldwide, various long-term sequelae have been observed in a large group of survivors. This paper is aimed at systematically analyzing user-generated conversations on Twitter that are related to long-term COVID symptoms for a better understanding of the Long COVID health consequences. Using an interactive information extraction tool built especially for this purpose, we extracted key information from the relevant tweets and analyzed the user-reported Long COVID symptoms with respect to their demographic and geographical characteristics. The results of our analysis are expected to improve the public awareness on long-term COVID-19 sequelae and provide important insights to public health authorities.

pdf bib
Bi-Directional Recurrent Neural Ordinary Differential Equations for Social Media Text Classification
Maunika Tamire | Srinivas Anumasa | P. K. Srijith

Classification of posts in social media such as Twitter is difficult due to the noisy and short nature of texts. Sequence classification models based on recurrent neural networks (RNN) are popular for classifying posts that are sequential in nature. RNNs assume the hidden representation dynamics to evolve in a discrete manner and do not consider the exact time of the posting. In this work, we propose to use recurrent neural ordinary differential equations (RNODE) for social media post classification which consider the time of posting and allow the computation of hidden representation to evolve in a time-sensitive continuous manner. In addition, we propose a novel model, Bi-directional RNODE (Bi-RNODE), which can consider the information flow in both the forward and backward directions of posting times to predict the post label. Our experiments demonstrate that RNODE and Bi-RNODE are effective for the problem of stance classification of rumours in social media.