Outline Generation: Understanding the Inherent Content Structure of Documents
Table 5. Evaluation results(%) of the section heading generation under Rougehead metric when the real sections are given aforehead.
['[EMPTY]', 'celebrity', 'cities', 'music', 'mixture']
Section heading prediction performance We evaluate the section heading generation ability to demonstrate the effectiveness of our Markov heading dependency mechanism and review mechanism. Here we suppose that sections in an article are already given, and only need to predict the corresponding headings for each section. We consider two generative baselines Hier and GHD, where GHD is an extension of Hier by employing a global heading dependency mechanism. We then introduce our Markov heading dependency mechanism based on the Hier, named HierMHD, and further employ the review mechanism, named HierMHD+RE. All these methods employ the section-aware attention in generation. We can find that: (1) Hier performs worst among all the methods, showing that the independence between context headings is not good for section heading generation. (2) By incorporating all the previous generated headings to model the dependence between context headings, GHD shows slight improvements on the heading generation performance. It indicates that the global dependency may not be effective in heading generation by involving too much context information, and also leads to high computing complexity. (3) The improvements of HierMHD over GHD indicate that the dependency between adjacent headings is sufficient for generating good and consistent section headings. (4) The improvements of HierMHD+RE over HierMHD demonstrate that the review mechanism is also helpful in improving the quality of section heading generation. All the improvements over the baselines are statistically significant (p-value < 0.01).