Lune

ACL Findings2021

Elaborative Simplification: Content Addition and Explanation Generation in Text Simplification

Neha Srikanth, Junyi Jessy Li

Abstract

Much of modern-day text simplification research focuses on sentence-level simplification, transforming original, more complex sentences into simplified versions.However, adding content can often be useful when difficult concepts and reasoning need to be explained.In this work, we present the first datadriven study of content addition in text simplification, which we call elaborative simplification.We introduce a new annotated dataset of 1.3K instances of elaborative simplification in the Newsela corpus, and analyze how entities, ideas, and concepts are elaborated through the lens of contextual specificity.We establish baselines for elaboration generation using large-scale pre-trained language models, and demonstrate that considering contextual specificity during generation can improve performance.Our results illustrate the complexities of elaborative simplification, suggesting many interesting directions for future work. Simplified Text Generated ElaborationsIt also has a high amount of smoking for a big city, according to a health department report.In Philadelphia, smoking is more common among the poor than people who aren't poor, said Giridhar Mallya.He works for the Philadelphia Department of Public Health.Being poor can be very hard on a person's mind.It is not easy to relax, or to feel positive. Contextual Specificity: HighGreedy: "It's not just the money," he said.Top-k: Mallya said the study showed that the poorest people in the city smoke a lot more than the richest people.Contextual: It can make them feel guilty.Workers at the Danish zoo said they had no choice but to kill the giraffe.The reason why is complicated.They said its genes were too much like the genes of other giraffes.All plants and animals have genes.They play a big part in what animals and plants look and act like.Genes are passed down from parents.They determine eye color, hair color, athletic ability, health and more. Contextual Specificity: LowGreedy: The scientists found that the giraffe's genes were very similar to those of other giraffes.Top-k: Scientists have known for years that the giraffe's genes were more like those of other giraffes. Contextual:The genes are passed down from generations.Because they look like a poisonous butterfly, they are left alone by predators.Looking like another species works just fine for them.While they avoid getting eaten, the male and the masculine-colored female are still at risk.For a long time, biologists have studied how animals try to look like other animals.It teaches them a lot about natural selection.Natural selection is sometimes called "survival of the fittest."It means that animals that can change will survive. Contextual Specificity: LowGreedy: It is a way of thinking about how animals live.Top-k: It means that the more an animal looks like another animal, the more likely it is to survive.Contextual: It's a way of thinking about how organisms adapt to change.The newly found cat is probably part of a separate branch of the cat family, said Tseng.The modern snow leopard is most likely part of the same branch.Both are able to live high in the Tibetan mountains.Big cats present serious problems for paleontologists.They have existed millions of years longer than people have. Contextual Specificity: HighGreedy: They are slow and slow to change.Top-k: Paleontologists study ancient animals.Contextual: They are difficult to study.Reporters and photographers watched.The big cat crept out of the cage.He looked around, and then ran off down a dirt road.The panther disappeared into the forest.Wildlife workers were setting him free. Contextual Specificity: HighGreedy: The news was not good.Top-k: Then, it disappeared into the darkness.

Ask Lune

Ask anything. The answer quotes the paper.

Lune reads all of it and the papers around it, then shows you the passages behind every answer.

Questions to start from

Lune answers from

the full paper · 14 top-tier papers that cite it · related work

Get the answer

Free to start. No credit card required.

Cited by top-tier papers14

  1. Multilingual Simplification of Medical TextsSebastian Joseph, Kathryn Kazanas, Keziah Reina, Vishnesh J. Ramanathan et al.EMNLP 2023 · 16 citations
  2. FactPICO: Factuality Evaluation for Plain Language Summarization of Medical EvidenceSebastian Joseph, Lily Chen, Jan Trienes, Hannah Louisa Göke et al.ACL 2024 · 12 citations
  3. Leveraging Large Language Models for Learning Complex Legal Concepts through StorytellingHang Jiang, Xiajie Zhang, Robert Mahari, Daniel T. Kessler et al.ACL 2024 · 12 citations
  4. Know Your Audience: The benefits and pitfalls of generating plain language summaries beyond the "general" audienceTal August, Kyle Lo, Noah A. Smith, Katharina ReineckeCHI 2024 · 11 citations
  5. APPLS: Evaluating Evaluation Metrics for Plain Language SummarizationYue Guo, Tal August, Gondy Leroy, Trevor Cohen et al.EMNLP 2024 · 7 citations

Builds on6

  1. BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and ComprehensionMike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad et al.ACL 2020 · 1,224 citations
  2. Evaluating Commonsense in Pre-Trained Language ModelsXuhui Zhou, Yue Zhang, Leyang Cui, Dandan HuangAAAI 2020 · 198 citations
  3. ToTTo: A Controlled Table-To-Text Generation DatasetAnkur P. Parikh, Xuezhi Wang, Sebastian Gehrmann, Manaal Faruqui et al.EMNLP 2020 · 69 citations
  4. Discourse Level Factors for Sentence Deletion in Text SimplificationYang Zhong, Chao Jiang, Wei Xu, Junyi Jessy LiAAAI 2020 · 57 citations
  5. Unsupervised Commonsense Question Answering with Self-TalkVered Shwartz, Peter West, Ronan Le Bras, Chandra Bhagavatula et al.EMNLP 2020 · 25 citations

Related papers

  1. Elaborative Simplification as Implicit Questions Under DiscussionYating Wu, William Sheffield, Kyle Mahowald, Junyi Jessy LiEMNLP 2023 · 4 citations
  2. Evaluating LLMs for Targeted Concept Simplification for Domain-Specific TextsSumit Asthana, Hannah Rashkin, Elizabeth Clark, Fantine Huot et al.EMNLP 2024 · 2 citations
  3. SWiPE: A Dataset for Document-Level Simplification of Wikipedia PagesPhilippe Laban, Jesse Vig, Wojciech Kryscinski, Shafiq Joty et al.ACL 2023 · 5 citations
  4. How May I Help You? Using Neural Text Simplification to Improve Downstream NLP TasksHoang Van, Zheng Tang, Mihai SurdeanuEMNLP Findings 2021 · 5 citations
  5. Elaboration-Generating Commonsense Question Answering at ScaleWenya Wang, Vivek Srikumar, Hannaneh Hajishirzi, Noah A. SmithACL 2023 · 5 citations
Dusk over the sea between two cliffs drawn in fine vertical lines