Text Summarization using Abstract Meaning Representation
Table 4: Results on CNN-Dailymail corpus. Table has 2 parts. First part contains baselines, our method and the state-of-the-art on the non-anonymized dataset, second part has scores on the anonymized dataset.
['Method', 'ROGUE 1 Recall', 'ROGUE 1 Precision', 'ROGUE 1', 'ROGUE 2', 'ROGUE L']
We present scores by using the first-3 model. The first row contains the Lead-3-AMR baseline. The results we achieve are competitive with the Lead-3-AMR baseline. The rest of the table contains scores of Lead-3 baseline followed by the state-of-the-art method on the anonymized and non-anonymized versions of the dataset. The drop in the scores from the Lead-3(non-anonymized) to Lead-3-AMR is significant and is largely because of the error introduced by parser and generator.