A Dynamic Evolutionary Framework for Timeline Generation based on Distributed Representations
Table 1. Performance of models on 17 Timelines
['Methods', 'ROUGE-1', 'ROUGE-2', 'ROUGE-S']
We can see that our proposed approach is quite comparable to other state-of-the-art models. It beats others by a large margin by ROUGE-1 and ROUGE-2. Chieuet al and ETS gives the lower F score, indicating too much handcraft features to constrain TS is restrictive. Tran et al and Regression are a typical idea of learning to rank, and Wang cast it as a sentence recommendation problem by matrix factorization. Thought they utilize the supervised information to help the model learning timeline rules, however, supervised model for timeline generation has its insufficiency that the development of timeline about different topic are many and varied. Our assumption about timeline is well reflected to achieve an ingenious combination of dynamic evolution and distributed representations in the entire framework. The best result we obtain demonstrates that the novel union of distributed representations can benefit the TS task.