Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document Inputs
Table 1: Answer Generation on ELI5 using Seq2Seq models receiving the Question and a support Document (e.g. TF-IDF selection, Triples, Linearized Graph) to produce the Answer. * denotes results from Fan et al. (2019).
['[BOLD] Model', '[BOLD] Input  [BOLD] Length', '[BOLD] ROUGE  [BOLD] 1', '[BOLD] ROUGE  [BOLD] 2', '[BOLD] ROUGE  [BOLD] L']
Using MMR to select the most relevant non-redundant input is similar to the TF-IDF baseline from Fan et al. The Seq2Seq Multi-task Triples baseline standardizes the input by forming triples but does not remove redundant triples. It produces marginally better results compared to the baseline Multi-Task model. Sub-selecting to the Top 20 Triples is harmful, as similar text has high TF-IDF overlap with the query so redundant information is selected. Creating the graph structure brings an improvement of around 0.6 ROUGE-1.