Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document Inputs
Table 4: Importance of Web Search Relevance on Validation for ELI5, modeling 850 input words.
['[BOLD] Model', '[BOLD] Input', '[BOLD] ROUGE-1']
Instead of constructing the graph from the first web search result to the last, we shuffle the web search results and construct the graph on this shuffled input. We compare this to modeling the web search results directly (no TF-IDF retrieval) and a model that receives this shuffled web search input. The graph is more robust to shuffling — as more information can be encoded in the graph due to its compression effect, the search hit ordering is less critical.