  Query-based open-domain NLP tasks require information synthesis from long and
diverse web results. Current approaches extractively select portions of web
text as input to Sequence-to-Sequence models using methods such as TF-IDF
ranking. We propose constructing a local graph structured knowledge base for
each query, which compresses the web search information and reduces redundancy.
We show that by linearizing the graph into a structured input sequence, models
can encode the graph representations within a standard Sequence-to-Sequence
setting. For two generative tasks with very long text input, long-form question
answering and multi-document summarization, feeding graph representations as
input can achieve better performance than using retrieved text portions.
