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Improving Truthfulness of Headline Generation

Improving Truthfulness of Headline Generation

2 May 2020
Kazuki Matsumaru
Sho Takase
Naoaki Okazaki
    HILM
ArXivPDFHTML

Papers citing "Improving Truthfulness of Headline Generation"

25 / 25 papers shown
Title
Fact-Preserved Personalized News Headline Generation
Fact-Preserved Personalized News Headline Generation
Zhao Yang
Junhong Lian
Xiang Ao
155
1
0
21 Jan 2025
Multi-News+: Cost-efficient Dataset Cleansing via LLM-based Data
  Annotation
Multi-News+: Cost-efficient Dataset Cleansing via LLM-based Data Annotation
Juhwan Choi
Jungmin Yun
Kyohoon Jin
Youngbin Kim
34
4
0
15 Apr 2024
Automatic Logical Forms improve fidelity in Table-to-Text generation
Automatic Logical Forms improve fidelity in Table-to-Text generation
Iñigo Alonso
Eneko Agirre
LMTD
22
3
0
26 Oct 2023
Zero-shot Faithfulness Evaluation for Text Summarization with Foundation
  Language Model
Zero-shot Faithfulness Evaluation for Text Summarization with Foundation Language Model
Qi Jia
Siyu Ren
Yizhu Liu
Kenny Q. Zhu
ALM
HILM
33
16
0
18 Oct 2023
NumHG: A Dataset for Number-Focused Headline Generation
NumHG: A Dataset for Number-Focused Headline Generation
Jian-Tao Huang
Chung-Chi Chen
Hen-Hsen Huang
Hsin-Hsi Chen
17
13
0
04 Sep 2023
HonestBait: Forward References for Attractive but Faithful Headline
  Generation
HonestBait: Forward References for Attractive but Faithful Headline Generation
Chih-Yao Chen
Dennis Wu
Lun-Wei Ku
30
2
0
26 Jun 2023
Expository Text Generation: Imitate, Retrieve, Paraphrase
Expository Text Generation: Imitate, Retrieve, Paraphrase
Nishant Balepur
Jie Huang
Kevin Chen-Chuan Chang
18
8
0
05 May 2023
Automatic Generation of Factual News Headlines in Finnish
Automatic Generation of Factual News Headlines in Finnish
Maximilian Koppatz
Khalid Alnajjar
Mika Hämäläinen
Thierry Poibeau
35
2
0
05 Dec 2022
Questioning the Validity of Summarization Datasets and Improving Their
  Factual Consistency
Questioning the Validity of Summarization Datasets and Improving Their Factual Consistency
Yanzhu Guo
Chloé Clavel
Moussa Kamal Eddine
Michalis Vazirgiannis
HILM
32
11
0
31 Oct 2022
Leveraging Key Information Modeling to Improve Less-Data Constrained
  News Headline Generation via Duality Fine-Tuning
Leveraging Key Information Modeling to Improve Less-Data Constrained News Headline Generation via Duality Fine-Tuning
Zhuoxuan Jiang
Lingfeng Qiao
Di Yin
Shanshan Feng
Bo Ren
SyDa
30
2
0
10 Oct 2022
Learning to Revise References for Faithful Summarization
Learning to Revise References for Faithful Summarization
Griffin Adams
Han-Chin Shing
Q. Sun
C. Winestock
Kathleen McKeown
Noémie Elhadad
19
32
0
13 Apr 2022
Don't Say What You Don't Know: Improving the Consistency of Abstractive
  Summarization by Constraining Beam Search
Don't Say What You Don't Know: Improving the Consistency of Abstractive Summarization by Constraining Beam Search
Daniel King
Zejiang Shen
Nishant Subramani
Daniel S. Weld
Iz Beltagy
Doug Downey
HILM
28
31
0
16 Mar 2022
Incorporating Question Answering-Based Signals into Abstractive
  Summarization via Salient Span Selection
Incorporating Question Answering-Based Signals into Abstractive Summarization via Salient Span Selection
Daniel Deutsch
Dan Roth
18
6
0
15 Nov 2021
CaPE: Contrastive Parameter Ensembling for Reducing Hallucination in
  Abstractive Summarization
CaPE: Contrastive Parameter Ensembling for Reducing Hallucination in Abstractive Summarization
Prafulla Kumar Choubey
Alexander R. Fabbri
Jesse Vig
Chien-Sheng Wu
Wenhao Liu
Nazneen Rajani
HILM
24
16
0
14 Oct 2021
Faithful or Extractive? On Mitigating the Faithfulness-Abstractiveness
  Trade-off in Abstractive Summarization
Faithful or Extractive? On Mitigating the Faithfulness-Abstractiveness Trade-off in Abstractive Summarization
Faisal Ladhak
Esin Durmus
He He
Claire Cardie
Kathleen McKeown
14
64
0
31 Aug 2021
The Factual Inconsistency Problem in Abstractive Text Summarization: A
  Survey
The Factual Inconsistency Problem in Abstractive Text Summarization: A Survey
Yi-Chong Huang
Xiachong Feng
Xiaocheng Feng
Bing Qin
HILM
136
106
0
30 Apr 2021
Extract, Denoise and Enforce: Evaluating and Improving Concept
  Preservation for Text-to-Text Generation
Extract, Denoise and Enforce: Evaluating and Improving Concept Preservation for Text-to-Text Generation
Yuning Mao
Wenchang Ma
Deren Lei
Jiawei Han
Xiang Ren
29
4
0
18 Apr 2021
Multi-Perspective Abstractive Answer Summarization
Multi-Perspective Abstractive Answer Summarization
Alexander R. Fabbri
Xiaojian Wu
Srini Iyer
Mona T. Diab
22
6
0
17 Apr 2021
What's in a Summary? Laying the Groundwork for Advances in
  Hospital-Course Summarization
What's in a Summary? Laying the Groundwork for Advances in Hospital-Course Summarization
Griffin Adams
Emily Alsentzer
Mert Ketenci
Jason Zucker
Noémie Elhadad
54
47
0
12 Apr 2021
Toward Improving Coherence and Diversity of Slogan Generation
Toward Improving Coherence and Diversity of Slogan Generation
Yiping Jin
Akshay Bhatia
Dittaya Wanvarie
Phu T. V. Le
19
5
0
11 Feb 2021
Constrained Abstractive Summarization: Preserving Factual Consistency
  with Constrained Generation
Constrained Abstractive Summarization: Preserving Factual Consistency with Constrained Generation
Yuning Mao
Xiang Ren
Heng Ji
Jiawei Han
HILM
125
38
0
24 Oct 2020
Improving Factual Completeness and Consistency of Image-to-Text
  Radiology Report Generation
Improving Factual Completeness and Consistency of Image-to-Text Radiology Report Generation
Yasuhide Miura
Yuhao Zhang
Emily Bao Tsai
C. Langlotz
Dan Jurafsky
MedIm
160
157
0
20 Oct 2020
Machines Getting with the Program: Understanding Intent Arguments of
  Non-Canonical Directives
Machines Getting with the Program: Understanding Intent Arguments of Non-Canonical Directives
Won Ik Cho
Y. Moon
Sangwhan Moon
Seokhwan Kim
N. Kim
22
6
0
01 Dec 2019
Sticking to the Facts: Confident Decoding for Faithful Data-to-Text
  Generation
Sticking to the Facts: Confident Decoding for Faithful Data-to-Text Generation
Ran Tian
Shashi Narayan
Thibault Sellam
Ankur P. Parikh
HILM
25
94
0
19 Oct 2019
Google's Neural Machine Translation System: Bridging the Gap between
  Human and Machine Translation
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
Yonghui Wu
M. Schuster
Zhehuai Chen
Quoc V. Le
Mohammad Norouzi
...
Alex Rudnick
Oriol Vinyals
G. Corrado
Macduff Hughes
J. Dean
AIMat
718
6,750
0
26 Sep 2016
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