Papers by Richard Susilo
Contextual Diversity Measure (CDM) for Controllable Story Generation in Large Language Models (2026.acl-srw)
Copied to clipboard
| Challenge: | Existing studies on controllable text generation focus on controlling attributes such as sentiment, writing style, and writing style. |
| Approach: | They introduce a metric that quantifies semantic diversity for scenario generation under fixed abstract semantic constraints and validate it through controlled experiments. |
| Outcome: | The proposed metric achieves excellent discrimination accuracy (100% and 91.9%, respectively), with discriminative power up to 5.5 greater than the best baseline. |
Text-to-Text Automatic Story Generation: A Survey (2026.eacl-srw)
Copied to clipboard
| Challenge: | Automated story generation aims to produce coherent, engaging, and contextually consistent narratives with minimal or no human involvement . despite advances in large language models, maintaining narrative coherence, character consistency, storyline diversity, and plot controllability in generating stories is still challenging. |
| Approach: | They propose to develop new evaluation metrics and better data sets to support automatic story generation. |
| Outcome: | The proposed evaluation metrics and better datasets will improve narrative coherence and consistency and explore practical applications of story generation. |