Text Summarization using Abstract Meaning Representation
Table 2: Table for analyzing the effect of using JAMR-Parser in step-1. This table has ROGUE scores after using Neural AMR for sentence generation i.e. step-3. First half contains scores by using gold-standard AMR graphs, second half has AMR graphs generated by JAMR-Parser
['Method', 'Rogue-1 Recall', 'Rogue-1 Precision', 'Rogue-1  [ITALIC] F1', 'Rogue-2', 'Rogue-L']
In this subsection we analyze the effect of using JAMR parser for step-1 instead of the gold-standard AMR graphs. We have used the same Neural AMR for sentence generation in all methods. Scores of all methods including the Lead-1-AMR baseline have dropped significantly. The aim of extracting summary graphs from the AMR graphs of the sentence is to drop the not so important information from the sentences. If we are able to achieve this perfectly, the ROGUE-1 Recall scores that we are getting should remain almost the same (since we are not add any new information) and the ROGUE-1 precision should go up (as we have thrown out some useless information); thus effectively improving the overall ROGUE-1 F1 score. we have the scores after using the full-AMR and extracted AMR for generation respectively. It is safe to say that extracting the AMR results in improved ROGUE-1 precision whereas ROGUE-1 Recall reduces only slightly, resulting in an overall improved ROGUE-1 F1.