Outline Generation: Understanding the Inherent Content Structure of Documents
Table 4. Comparisons between our HiStGen and step-wise baselines in terms of EMoutline(%).
['[EMPTY]', 'celebrity', 'cities', 'music', 'mixture']
We have the following observations: (1) The GA process (i.e., GATextRank, GATopicRank, GAHier and GAGHD) performs very poorly. By looking at the results of the GA methods, we find that GA tends to segment the document into too much sections since it usually generates different headings even for paragraphs that should belong to a same section. (2) For the IG process, the methods based on CRF perform better than that based on GPD. For example, the relative improvement of IGCRF+GHD over IGGPD+GHD is about 13.7% in terms of EMoutline on the mixture set. We analyze the results and find that using CRF can obtain better section prediction results, showing that the dependency on the context labels is more important than that on all the paragraphs for section identification. Moreover, for the IG process, the generative methods can achieve significantly better results than the extractive methods, since those extractive methods are unsupervised in nature. (3) Our HiStGen model can outperform all the step-wise baselines significantly (p-value < 0.01). As compared with the best-performing baseline IGCRF+GHD, the relative improvement of HiStGen over IGCRF+GHD is about 27.6% in terms of EMoutline on the mixture set. The results demonstrate the effectiveness of our end-to-end learning model.