Papers › Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document Inputs
Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document Inputs
Angela Fan, Claire Gardent, Chloe Braud, Antoine Bordes
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.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Open-Domain Question Answering | ELI5 | E-MCA | Rouge-1 | 30.0 | #4 of 6 | Archive leaderboard | report |
| Open-Domain Question Answering | ELI5 | E-MCA | Rouge-2 | 5.8 | #4 of 6 | Archive leaderboard | report |
| Open-Domain Question Answering | ELI5 | E-MCA | Rouge-L | 24.0 | #4 of 6 | Archive leaderboard | report |
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