Conditional Neural Generation using Sub-Aspect Functions for Extractive News Summarization
Table V: Inference scores on AMI corpus from baselines and our model with control codes, in the form of [importance, diversity, position]. * denotes the results from corresponding paper.
['[EMPTY]', '[BOLD] R-1 F1', '[BOLD] R-2 F1', '[BOLD] R-2 Recall']
We also conducted an inference experiment on a less position-biased corpus. Different from news summarization, the meeting summaries are usually more abstractive with keywords extracted from the whole conversation. Not surprisingly, summaries under position code do not perform well as the corpus is more balanced. This shows the effectiveness of semantic-related control codes and the generality of our model.