@inproceedings{fan-etal-2019-using,
title = "Using Local Knowledge Graph Construction to Scale {S}eq2{S}eq Models to Multi-Document Inputs",
author = "Fan, Angela and
Gardent, Claire and
Braud, Chlo{\'e} and
Bordes, Antoine",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1428/",
doi = "10.18653/v1/D19-1428",
pages = "4186--4196",
abstract = "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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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document Inputs
%A Fan, Angela
%A Gardent, Claire
%A Braud, Chloé
%A Bordes, Antoine
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F fan-etal-2019-using
%X 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.
%R 10.18653/v1/D19-1428
%U https://aclanthology.org/D19-1428/
%U https://doi.org/10.18653/v1/D19-1428
%P 4186-4196
Markdown (Informal)
[Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document Inputs](https://aclanthology.org/D19-1428/) (Fan et al., EMNLP-IJCNLP 2019)
ACL