# Controlled Text Reduction arXiv: https://arxiv.org/abs/2210.13449 ยท HTML: https://mlsanity.com/paper/2210.13449/ ## Key facts - Cited by 10 papers in the map (data through 14 Oct 2026). - Builds on 43 of its 71 references that are in the map. - First posted on arXiv 24 Oct 2022. ## Builds on (most cited first, 30 of 43) - [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019) - [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020) - [BERT Rediscovers the Classical NLP Pipeline](https://mlsanity.com/paper/1905.05950/) (2019) - [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://mlsanity.com/paper/1602.06023/) (2016) - [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://mlsanity.com/paper/1909.05858/) (2019) - [SummEval: Re-evaluating Summarization Evaluation](https://mlsanity.com/paper/2007.12626/) (2020) - [A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents](https://mlsanity.com/paper/1804.05685/) (2018) - [What you can cram into a single vector: Probing sentence embeddings for linguistic properties](https://mlsanity.com/paper/1805.01070/) (2018) - [What do you learn from context? Probing for sentence structure in contextualized word representations](https://mlsanity.com/paper/1905.06316/) (2019) - [Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model](https://mlsanity.com/paper/1906.01749/) (2019) - [SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarization](https://mlsanity.com/paper/2111.09525/) (2021) - [A Deep Reinforced Model for Abstractive Summarization](https://mlsanity.com/paper/1705.04304/) (2017) - [Bottom-Up Abstractive Summarization](https://mlsanity.com/paper/1808.10792/) (2018) - [Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting](https://mlsanity.com/paper/1805.11080/) (2018) - [Q2: Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering](https://mlsanity.com/paper/2104.08202/) (2021) - [Abstractive Summarization of Reddit Posts with Multi-level Memory Networks](https://mlsanity.com/paper/1811.00783/) (2018) - [CTRLsum: Towards Generic Controllable Text Summarization](https://mlsanity.com/paper/2012.04281/) (2020) - [Planning with Learned Entity Prompts for Abstractive Summarization](https://mlsanity.com/paper/2104.07606/) (2021) - [A Large-Scale Multi-Document Summarization Dataset from the Wikipedia Current Events Portal](https://mlsanity.com/paper/2005.10070/) (2020) - [AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document Summarization](https://mlsanity.com/paper/2010.12694/) (2020) - [Query Focused Abstractive Summarization: Incorporating Query Relevance, Multi-Document Coverage, and Summary Length Constraints into seq2seq Models](https://mlsanity.com/paper/1801.07704/) (2018) - [Crowdsourcing Lightweight Pyramids for Manual Summary Evaluation](https://mlsanity.com/paper/1904.05929/) (2019) - [Controlled Crowdsourcing for High-Quality QA-SRL Annotation](https://mlsanity.com/paper/1911.03243/) (2019) - [DiscoFuse: A Large-Scale Dataset for Discourse-Based Sentence Fusion](https://mlsanity.com/paper/1902.10526/) (2019) - [Enhancing Machine Translation with Dependency-Aware Self-Attention](https://mlsanity.com/paper/1909.03149/) (2019) - [Improving the Similarity Measure of Determinantal Point Processes for Extractive Multi-Document Summarization](https://mlsanity.com/paper/1906.00072/) (2019) - [Summary-Source Proposition-level Alignment: Task, Datasets and Supervised Baseline](https://mlsanity.com/paper/2009.00590/) (2020) - [Syntax-Infused Transformer and BERT models for Machine Translation and Natural Language Understanding](https://mlsanity.com/paper/1911.06156/) (2019) - [Syntactically Supervised Transformers for Faster Neural Machine Translation](https://mlsanity.com/paper/1906.02780/) (2019) - [Analyzing Sentence Fusion in Abstractive Summarization](https://mlsanity.com/paper/1910.00203/) (2019) ## Built on by (most cited first, 10 of 10) - [Attribute First, then Generate: Locally-attributable Grounded Text Generation](https://mlsanity.com/paper/2403.17104/) (2024) - [Concise Answers to Complex Questions: Summarization of Long-form Answers](https://mlsanity.com/paper/2305.19271/) (2023) - [The Power of Summary-Source Alignments](https://mlsanity.com/paper/2406.00842/) (2024) - [Revisiting Sentence Union Generation as a Testbed for Text Consolidation](https://mlsanity.com/paper/2305.15605/) (2023) - [USB: A Unified Summarization Benchmark Across Tasks and Domains](https://mlsanity.com/paper/2305.14296/) (2023) - [Context-Aware Document Simplification](https://mlsanity.com/paper/2305.06274/) (2023) - [SummHelper: Collaborative Human-Computer Summarization](https://mlsanity.com/paper/2308.08363/) (2023) - [Attributable by Construction: Claim-Anchored Provenance for Multi-Document Summarization](https://mlsanity.com/paper/2606.23989/) (2026) - [Dont Add, dont Miss: Effective Content Preserving Generation from Pre-Selected Text Spans](https://mlsanity.com/paper/2310.09017/) (2023) - [Multi-Review Fusion-in-Context](https://mlsanity.com/paper/2403.15351/) (2024) ## Abstract (arXiv) Producing a reduced version of a source text, as in generic or focused summarization, inherently involves two distinct subtasks: deciding on targeted content and generating a coherent text conveying it. While some popular approaches address summarization as a single end-to-end task, prominent works support decomposed modeling for individual subtasks. Further, semi-automated text reduction is also very appealing, where users may identify targeted content while models would generate a corresponding coherent summary. In this paper, we focus on the second subtask, of generating coherent text given pre-selected content. Concretely, we formalize textitControlled Text Reduction as a standalone task, whose input is a source text with marked spans of targeted content ("highlighting"). A model then needs to generate a coherent text that includes all and only the target information. We advocate the potential of such models, both for modular fully-automatic summarization, as well as for semi-automated human-in-the-loop use cases. Facilitating proper research, we crowdsource high-quality dev and test datasets for the task. Further, we automatically generate a larger "silver" training dataset from available summarization benchmarks, leveraging a pretrained summary-source alignment model. Finally, employing these datasets, we present a supervised baseline model, showing promising results and insightful analyses.