Conditional Neural Generation using Sub-Aspect Functions for Extractive News Summarization
Table II: ROUGE F1 score evaluation with various control codes, in the form of [importance, diversity, position]. * denotes the results from corresponding paper.
['[EMPTY]', '[BOLD] ROUGE-1', '[BOLD] ROUGE-2']
(1) Summary generated from code [0,0,1] is similar to LEAD-3 but can dynamically learn the positional features not limited to the first 3 sentences, while isolating out diversity and importance features. (2) Only focusing on the importance sub-aspect leads to the worst performance, but performance can be improved when considering other sub-aspects. (3) Focusing on the diversity sub-aspect alone (i.e. Code [0,1,0]) can generate results comparable to strong baselines such as SummaRuNNer.