2021
Shirani, Amirreza; Tran, Giai; Trinh, Hieu; Dernoncourt, Franck; Lipka, Nedim; Echevarria, Jose; Solorio, Thamar; Asente, Paul
PSED: A Dataset for Selecting Emphasis in Presentation Slides Inproceedings
In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pp. 4314–4320, 2021.
Links | BibTeX | Tags: Emphasis Selection
@inproceedings{shirani-etal-2021-psed,
title = {PSED: A Dataset for Selecting Emphasis in Presentation Slides},
author = { Amirreza Shirani and Giai Tran and Hieu Trinh and Franck Dernoncourt and Nedim Lipka and Jose Echevarria and Thamar Solorio and Paul Asente},
url = {https://aclanthology.org/2021.findings-acl.377},
doi = {10.18653/v1/2021.findings-acl.377},
year = {2021},
date = {2021-01-01},
booktitle = {Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021},
pages = {4314--4320},
keywords = {Emphasis Selection},
pubstate = {published},
tppubtype = {inproceedings}
}
2020
Shirani, Amirreza; Dernoncourt, Franck; Lipka, Nedim; Asente, Paul; Echevarria, Jose; Solorio, Thamar
SemEval-2020 Task 10: Emphasis Selection for Written Text in Visual Media Conference
Proceedings of the Fourteenth Workshop on Semantic Evaluation, International Committee for Computational Linguistics, Barcelona (online), 2020.
Abstract | Links | BibTeX | Tags: Emphasis Selection, SemEval
@conference{shirani-etal-2020-semeval,
title = {SemEval-2020 Task 10: Emphasis Selection for Written Text in Visual Media},
author = {Amirreza Shirani and Franck Dernoncourt and Nedim Lipka and Paul Asente and Jose Echevarria and Thamar Solorio},
url = {https://www.aclweb.org/anthology/2020.semeval-1.184},
year = {2020},
date = {2020-12-03},
booktitle = {Proceedings of the Fourteenth Workshop on Semantic Evaluation},
publisher = {International Committee for Computational Linguistics},
address = {Barcelona (online)},
abstract = {In this paper, we present the main findings and compare the results of SemEval-2020 Task 10, Emphasis Selection for Written Text in Visual Media. The goal of this shared task is to design automatic methods for emphasis selection, i.e. choosing candidates for emphasis in textual content to enable automated design assistance in authoring. The main focus is on short text instances for social media, with a variety of examples, from social media posts to inspirational quotes. Participants were asked to model emphasis using plain text with no additional context from the user or other design considerations. SemEval-2020 Emphasis Selection shared task attracted 197 participants in the early phase and a total of 31 teams made submissions to this task. The highest-ranked submission achieved 0.823 Matchm score. The analysis of systems submitted to the task indicates that BERT and RoBERTa were the most common choice of pre-trained models used, and part of speech tag (POS) was the most useful feature. Full results can be found on the task's website.},
keywords = {Emphasis Selection, SemEval},
pubstate = {published},
tppubtype = {conference}
}
In this paper, we present the main findings and compare the results of SemEval-2020 Task 10, Emphasis Selection for Written Text in Visual Media. The goal of this shared task is to design automatic methods for emphasis selection, i.e. choosing candidates for emphasis in textual content to enable automated design assistance in authoring. The main focus is on short text instances for social media, with a variety of examples, from social media posts to inspirational quotes. Participants were asked to model emphasis using plain text with no additional context from the user or other design considerations. SemEval-2020 Emphasis Selection shared task attracted 197 participants in the early phase and a total of 31 teams made submissions to this task. The highest-ranked submission achieved 0.823 Matchm score. The analysis of systems submitted to the task indicates that BERT and RoBERTa were the most common choice of pre-trained models used, and part of speech tag (POS) was the most useful feature. Full results can be found on the task’s website.
2019
Shirani, Amirreza; Dernoncourt, Franck; Asente, Paul; Lipka, Nedim; Kim, Seokhwan; Echevarria, Jose; Solorio, Thamar
Learning Emphasis Selection for Written Text in Visual Media from Crowd-Sourced Label Distributions Conference
The 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019), 2019.
Abstract | Links | BibTeX | Tags: Emphasis Selection
@conference{Shirani2019,
title = {Learning Emphasis Selection for Written Text in Visual Media from Crowd-Sourced Label Distributions},
author = {Amirreza Shirani and Franck Dernoncourt and Paul Asente and Nedim Lipka and Seokhwan Kim and Jose Echevarria and Thamar Solorio},
url = {https://www.aclweb.org/anthology/papers/P/P19/P19-1112/},
year = {2019},
date = {2019-06-10},
booktitle = {The 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019)},
abstract = {In visual communication, text emphasis is used to increase the comprehension of written text and to convey the author’s intent. We study the problem of emphasis selection, i.e. choosing candidates for emphasis in short written text, to enable automated design assistance in authoring. Without knowing the author’s intent and only considering the input text, multiple emphasis selections are valid. We propose a model that employs end-to-end label distribution learning (LDL) on crowd-sourced data and predicts a selection distribution, capturing the inter-subjectivity (common-sense) in the audience as well as the ambiguity of the input. We compare the model with several baselines in which the problem is transformed to single-label learning by mapping label distributions to absolute labels via majority voting.},
keywords = {Emphasis Selection},
pubstate = {published},
tppubtype = {conference}
}
In visual communication, text emphasis is used to increase the comprehension of written text and to convey the author’s intent. We study the problem of emphasis selection, i.e. choosing candidates for emphasis in short written text, to enable automated design assistance in authoring. Without knowing the author’s intent and only considering the input text, multiple emphasis selections are valid. We propose a model that employs end-to-end label distribution learning (LDL) on crowd-sourced data and predicts a selection distribution, capturing the inter-subjectivity (common-sense) in the audience as well as the ambiguity of the input. We compare the model with several baselines in which the problem is transformed to single-label learning by mapping label distributions to absolute labels via majority voting.