Papers by Maria Glenski
Unsupervised Keyphrase Extraction via Interpretable Neural Networks (2023.findings-eacl)
Copied to clipboard
Rishabh Joshi, Vidhisha Balachandran, Emily Saldanha, Maria Glenski, Svitlana Volkova, Yulia Tsvetkov
| Challenge: | Prior approaches for unsupervised keyphrase extraction relied on heuristic notions of phrase importance via embedding clustering or graph centrality. |
| Approach: | They propose an approach which defines keyphrases as document phrases that are salient for predicting the topic of the document. |
| Outcome: | The proposed method alleviates the need for ad-hoc heuristics and achieves state-of-the-art results in scientific publications and news articles. |
Identifying and Understanding User Reactions to Deceptive and Trusted Social News Sources (P18-2)
Copied to clipboard
| Challenge: | a new study examines how users react to news sources with different levels of credibility . a recent study found that 59% of bitly-URLs on Twitter are shared without ever being read . |
| Approach: | They develop a model to classify user reactions into one of nine types . they also measure the speed and type of reaction for trusted and deceptive news sources . |
| Outcome: | The proposed model classifies user reactions into one of nine types, such as answer, elaboration, and question, etc. |