Structured Text Summarization via Open Domain Information Extraction

Hao, Zengguang; Xu, Binxia; Zheng, Shiyuan; Gao, Yang

doi:10.1109/cscwd.2018.8465372

Cited by 1 publication

(1 citation statement)

References 8 publications

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“…Not relying on any predefined schema, OpenIE systems typically extract information as (subject; relation; object) triples. The extracted information is then used in several downstream applications, including answering questions (Khot et al, 2017), summarizing documents (Hao et al, 2018;Ji et al, 2013), and populating knowledge bases (Fan et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

CompactIE: Compact Facts in Open Information Extraction

Fatahi¹,

Bhutani²,

Jagadish³

2022

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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A major drawback of modern neural OpenIE systems and benchmarks is that they prioritize high coverage of information in extractions over compactness of their constituents. This severely limits the usefulness of OpenIE extractions in many downstream tasks. The utility of extractions can be improved if extractions are compact and share constituents. To this end, we study the problem of identifying compact extractions with neural-based methods. We propose COMPACTIE, an OpenIE system that uses a novel pipelined approach to produce compact extractions with overlapping constituents. It first detects constituents of the extractions and then links them to build extractions. We train our system on compact extractions obtained by processing existing benchmarks. Our experiments on CaRB and Wire57 datasets indicate that COMPACTIE finds 1.5x-2x more compact extractions than previous systems, with high precision, establishing a new state-of-the-art performance in OpenIE.

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