Distilling Salient Reviews with Zero Labels Article Swipe
Chieh-Yang Huang
,
Jinfeng Li
,
Nikita Bhutani
,
Alexander Whedon
,
Estevam Hruschka
,
Yoshi Suhara
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.18653/v1/2022.fever-1.3
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.18653/v1/2022.fever-1.3
Many people read online reviews to learn about real-world entities of their interest. However, majority of reviews only describes general experiences and opinions of the customers, and may not reveal facts that are specific to the entity being reviewed. In this work, we focus on a novel task of mining from a review corpus sentences that are unique for each entity. We refer to this task as Salient Fact Extraction. Salient facts are extremely scarce due to their very nature. Consequently, collecting labeled examples for training supervised models is tedious and cost-prohibitive. To alleviate this scarcity problem, we develop an unsupervised method, ZL-Distiller, which leverages contextual language representations of the reviews and their distributional patterns to identify salient sentences about entities. Our experiments on multiple domains (hotels, products, and restaurants) show that ZL-Distiller achieves state-of-the-art performance and further boosts the performance of other supervised/unsupervised algorithms for the task. Furthermore, we show that salient sentences mined by ZL-Distiller provide unique and detailed information about entities, which benefit downstream NLP applications including question answering and summarization.
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Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.18653/v1/2022.fever-1.3
- https://aclanthology.org/2022.fever-1.3.pdf
- OA Status
- gold
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4285215521
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4285215521Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.18653/v1/2022.fever-1.3Digital Object Identifier
- Title
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Distilling Salient Reviews with Zero LabelsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-01-01Full publication date if available
- Authors
-
Chieh-Yang Huang, Jinfeng Li, Nikita Bhutani, Alexander Whedon, Estevam Hruschka, Yoshi SuharaList of authors in order
- Landing page
-
https://doi.org/10.18653/v1/2022.fever-1.3Publisher landing page
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https://aclanthology.org/2022.fever-1.3.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
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-
goldOpen access status per OpenAlex
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https://aclanthology.org/2022.fever-1.3.pdfDirect OA link when available
- Concepts
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Automatic summarization, Salient, Computer science, Task (project management), Natural language processing, Artificial intelligence, Focus (optics), Scarcity, Information extraction, Machine learning, Economics, Optics, Physics, Management, MicroeconomicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
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25Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.mined | 154 |
| abstract_inverted_index.novel | 46 |
| abstract_inverted_index.other | 142 |
| abstract_inverted_index.refer | 62 |
| abstract_inverted_index.task. | 147 |
| abstract_inverted_index.their | 11, 77, 112 |
| abstract_inverted_index.which | 103, 164 |
| abstract_inverted_index.work, | 41 |
| abstract_inverted_index.boosts | 138 |
| abstract_inverted_index.corpus | 53 |
| abstract_inverted_index.entity | 36 |
| abstract_inverted_index.mining | 49 |
| abstract_inverted_index.models | 87 |
| abstract_inverted_index.online | 3 |
| abstract_inverted_index.people | 1 |
| abstract_inverted_index.reveal | 29 |
| abstract_inverted_index.review | 52 |
| abstract_inverted_index.scarce | 74 |
| abstract_inverted_index.unique | 57, 158 |
| abstract_inverted_index.Salient | 67, 70 |
| abstract_inverted_index.benefit | 165 |
| abstract_inverted_index.develop | 98 |
| abstract_inverted_index.domains | 125 |
| abstract_inverted_index.entity. | 60 |
| abstract_inverted_index.further | 137 |
| abstract_inverted_index.general | 19 |
| abstract_inverted_index.labeled | 82 |
| abstract_inverted_index.method, | 101 |
| abstract_inverted_index.nature. | 79 |
| abstract_inverted_index.provide | 157 |
| abstract_inverted_index.reviews | 4, 16, 110 |
| abstract_inverted_index.salient | 117, 152 |
| abstract_inverted_index.tedious | 89 |
| abstract_inverted_index.(hotels, | 126 |
| abstract_inverted_index.However, | 13 |
| abstract_inverted_index.achieves | 133 |
| abstract_inverted_index.detailed | 160 |
| abstract_inverted_index.entities | 9 |
| abstract_inverted_index.examples | 83 |
| abstract_inverted_index.identify | 116 |
| abstract_inverted_index.language | 106 |
| abstract_inverted_index.majority | 14 |
| abstract_inverted_index.multiple | 124 |
| abstract_inverted_index.opinions | 22 |
| abstract_inverted_index.patterns | 114 |
| abstract_inverted_index.problem, | 96 |
| abstract_inverted_index.question | 170 |
| abstract_inverted_index.scarcity | 95 |
| abstract_inverted_index.specific | 33 |
| abstract_inverted_index.training | 85 |
| abstract_inverted_index.alleviate | 93 |
| abstract_inverted_index.answering | 171 |
| abstract_inverted_index.describes | 18 |
| abstract_inverted_index.entities, | 163 |
| abstract_inverted_index.entities. | 120 |
| abstract_inverted_index.extremely | 73 |
| abstract_inverted_index.including | 169 |
| abstract_inverted_index.interest. | 12 |
| abstract_inverted_index.leverages | 104 |
| abstract_inverted_index.products, | 127 |
| abstract_inverted_index.reviewed. | 38 |
| abstract_inverted_index.sentences | 54, 118, 153 |
| abstract_inverted_index.algorithms | 144 |
| abstract_inverted_index.collecting | 81 |
| abstract_inverted_index.contextual | 105 |
| abstract_inverted_index.customers, | 25 |
| abstract_inverted_index.downstream | 166 |
| abstract_inverted_index.real-world | 8 |
| abstract_inverted_index.supervised | 86 |
| abstract_inverted_index.Extraction. | 69 |
| abstract_inverted_index.experiences | 20 |
| abstract_inverted_index.experiments | 122 |
| abstract_inverted_index.information | 161 |
| abstract_inverted_index.performance | 135, 140 |
| abstract_inverted_index.Furthermore, | 148 |
| abstract_inverted_index.ZL-Distiller | 132, 156 |
| abstract_inverted_index.applications | 168 |
| abstract_inverted_index.restaurants) | 129 |
| abstract_inverted_index.unsupervised | 100 |
| abstract_inverted_index.Consequently, | 80 |
| abstract_inverted_index.ZL-Distiller, | 102 |
| abstract_inverted_index.distributional | 113 |
| abstract_inverted_index.summarization. | 173 |
| abstract_inverted_index.representations | 107 |
| abstract_inverted_index.state-of-the-art | 134 |
| abstract_inverted_index.cost-prohibitive. | 91 |
| abstract_inverted_index.supervised/unsupervised | 143 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.07420181 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |