Interactive Editing for Text Summarization
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Abstract (arXiv)
Summarizing lengthy documents is a common and essential task in our daily lives. Although recent advancements in neural summarization models can assist in crafting general-purpose summaries, human writers often have specific requirements that call for a more customized approach. To address this need, we introduce REVISE (Refinement and Editing via Iterative Summarization Enhancement), an innovative framework designed to facilitate iterative editing and refinement of draft summaries by human writers. Within our framework, writers can effortlessly modify unsatisfactory segments at any location or length and provide optional starting phrases -- our system will generate coherent alternatives that seamlessly integrate with the existing summary. At its core, REVISE incorporates a modified fill-in-the-middle model with the encoder-decoder architecture while developing novel evaluation metrics tailored for the summarization task. In essence, our framework empowers users to create high-quality, personalized summaries by effectively harnessing both human expertise and AI capabilities, ultimately transforming the summarization process into a truly collaborative and adaptive experience.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 32 of its 50 references that are in the map. First posted on arXiv 5 Jun 2023.
Builds on
32 of 50 references.
References · 32
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2023GPT-4 Technical Reportcited by 18,132
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
- 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,801
- 2017Get To The Point: Summarization with Pointer-Generator Networkscited by 1,281
- 2019PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarizationcited by 1,100
- 2021GLM: General Language Model Pretraining with Autoregressive Blank Infillingcited by 1,026
- 2019SpanBERT: Improving Pre-training by Representing and Predicting Spanscited by 832
- 2021BARTScore: Evaluating Generated Text as Text Generationcited by 719
- 2022InCoder: A Generative Model for Code Infilling and Synthesiscited by 492
- 2022Efficient Training of Language Models to Fill in the Middlecited by 248
20 more
- 2022CM3: A Causal Masked Multimodal Model of the Internetcited by 142
- 2019Insertion Transformer: Flexible Sequence Generation via Insertion Operationscited by 123
- 2020Enabling Language Models to Fill in the Blankscited by 121
- 2019Gmail Smart Compose: Real-Time Assisted Writingcited by 97
- 2022Evaluating Human-Language Model Interactioncited by 90
- 2018MaskGAN: Better Text Generation via Filling in the______cited by 89
- 2019Insertion-based Decoding with automatically Inferred Generation Ordercited by 43
- 2020Blank Language Modelscited by 43
- 2017Task-Oriented Query Reformulation with Reinforcement Learningcited by 39
- 2019Text Infillingcited by 32
- 2022Z-Code++: A Pre-trained Language Model Optimized for Abstractive Summarizationcited by 29
- 2019KERMIT: Generative Insertion-Based Modeling for Sequencescited by 21
- 2018Active Learning for Interactive Neural Machine Translation of Data Streamscited by 20
- 2022Mapping the Design Space of Human-AI Interaction in Text Summarizationcited by 15
- 2019Preference-based Interactive Multi-Document Summarisationcited by 11
- 2019TIGS: An Inference Algorithm for Text Infilling with Gradient Searchcited by 10
- 2018Online Learning for Effort Reduction in Interactive Neural Machine Translationcited by 9
- 2019Correct-and-Memorize: Learning to Translate from Interactive Revisionscited by 6
- 2021Hone as You Read: A Practical Type of Interactive Summarizationcited by 4
- 2019Interactive-predictive neural multimodal systemscited by 1
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Citing papers per year: 2023: 1; 2024: 0; 2025: 1.
2 papers.