Diverse, Controllable, and Keyphrase-Aware:A Corpus and Method for News Multi-Headline Generation
Table 2: Multi-Headline Generation Results
['Method', 'ROUGE-1 k=1', 'ROUGE-1 k=3', 'ROUGE-1 k=5', 'ROUGE-2 k=1', 'ROUGE-2 k=3', 'ROUGE-2 k=5', 'ROUGE-L k=1', 'ROUGE-L k=3', 'ROUGE-L k=5', 'Distinct-1 k=3', 'Distinct-1 k=5', 'Distinct-2 k=3', 'Distinct-2 k=5']
Results. For diversity, we can see that all of our keyphrase-aware generation models significantly outperform than other keyphrase-agnostic baselines in both Distinct-1 and Distinct-2 metrics for all K. After using the diverse decoding, BASE + Diverse achieves higher diversity. Nevertheless, its diversity is still lower than most keyphrase-aware generation models. As expected, BASE + Filter achieves the highest diversity, and BASE + KEY achieves the lowest diversity among the variants of our keyphrase-aware generation models. For quality, except BASE, there is still a big gap between other keyphrase-agnostic baselines and our keyphrase-aware generation models. Except for BASE + Filter, all of our keyphrase-aware generation models achieve higher ROUGE scores than BASE and BASE + Diverse These results show that our keyphrase-aware generation models can effectively generate high-quality and diverse headlines.