Papers with news datasets
The Change that Matters in Discourse Parsing: Estimating the Impact of Domain Shift on Parser Error (2022.findings-acl)
Copied to clipboard
| Challenge: | Discourse analysis is very low on texts outside of the training distribution’s coverage, diminishing the practical utility of existing models. |
| Approach: | They propose to use a distribution shift statistic to estimate the error-gap of a discourse model and to use it to estimate it. |
| Outcome: | The proposed model can be estimated via distribution shift but does not correlate with change in the observed error of a classifier (i.e. error-gap). |
Headline Token-based Discriminative Learning for Subheading Generation in News Article (2023.findings-eacl)
Copied to clipboard
| Challenge: | Existing models that generate news subheadings rely on topical headline information to capture topical knowledge from the article. |
| Approach: | They propose a model that uses topical headline information to generate news subheadings using masked headline tokens. |
| Outcome: | The proposed model outperforms the comparative models on three news datasets written in two languages and performs robustly on a small dataset and various masking ratios. |
Question Answering as an Automatic Evaluation Metric for News Article Summarization (N19-1)
Copied to clipboard
| Challenge: | Recent work on summarization and headline generation focuses on maximizing ROUGE scores. |
| Approach: | They propose an extrinsic evaluation metric that maximizes ROUGE scores for automatic summarization and headline generation. |
| Outcome: | The proposed model maximizes ROUGE scores while increasing competitive results. |