natural-language-processing-2

Query-Based Extractive Summarization

3260 papers • 126 benchmarks • 313 datasets

Extracting summarized information that answers a given query based on a reference text.

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Benchmarks

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Actions

Debatepedia

Libraries

i

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Datasets

DebateSum

SubSumE

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Most implemented papers

DebateSum: A large-scale argument mining and summarization dataset

Allen Roush, Arvind Balaji•Fri Nov 13 2020

The DebateSum dataset, which consists of 187,386 unique pieces of evidence with corresponding argument and extractive summaries, is presented and a search engine for this dataset is presented, utilized extensively by members of the National Speech and Debate Association today.

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Diversity driven attention model for query-based abstractive summarization

Balaraman Ravindran, Mitesh M. Khapra, Preksha Nema, Anirban Laha•Tue Apr 25 2017

This work proposes a model for the query-based summarization task based on the encode-attend-decode paradigm with two key additions: a query attention model which learns to focus on different portions of the query at different time steps and a new diversity based Attention model which aims to alleviate the problem of repeating phrases in the summary.

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CX DB8: A queryable extractive summarizer and semantic search engine

Allen Roush•Sun Dec 06 2020

CX_DB8 is introduced, a queryable word-level extractive summarizer and evidence creation framework, which allows for rapid, biasable summarization of arbitarily sized texts and also functions as a semantic search engine.

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