| Challenge: | Data-to-Text Generation (D2T) problems can be seen as a stream of time-stamped events with a textual summary of each event presenting the insights. |
| Approach: | They propose a typology of content types to classify the contents of event summaries using a dataset as the distribution of the aggregated content types. |
| Outcome: | The proposed typology shows that neural systems struggle in generating complex types on different datasets. |
Similar Papers
Have Your Text and Use It Too! End-to-End Neural Data-to-Text Generation with Semantic Fidelity (2020.coling-main)
Copied to clipboard
| Challenge: | End-to-end neural data-totext generation has faced challenges generalizing to new domains and generating semantically consistent text. |
| Approach: | They propose a neural data-to-text generation system that makes minimal assumptions about the data representation and target domain. |
| Outcome: | The proposed system achieves state of the art results on four major D2T datasets with better semantic fidelity than the state-of-the-art methods. |
D2S: Document-to-Slide Generation Via Query-Based Text Summarization (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing research efforts to automate the document-to-slide generation process face a critical challenge: no publicly available dataset for training and benchmarking. |
| Approach: | They propose a dataset SciDuet that gathers papers and their corresponding slides from recent years’ NLP and ML conferences. |
| Outcome: | The proposed system outperforms state-of-the-art summarization baselines on both automated ROUGE metrics and qualitative human evaluation. |
Compositional Generalization for Data-to-Text Generation (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Data-to-text generation models can be used to generate textual descriptions from structured data . despite advances, systems struggle when confronted with unseen combinations of predicates . |
| Approach: | They propose a data-to-text generation model that addresses compositional generalization by clustering predicates into groups. |
| Outcome: | The proposed model outperforms T5-baselines in all evaluation metrics. |
Controllable Text Summarization: Unraveling Challenges, Approaches, and Prospects - A Survey (2024.findings-acl)
Copied to clipboard
| Challenge: | scholarly attention has turned to the development of text summarization methods that are more closely tailored and controlled to align with specific objectives and user needs. |
| Approach: | They formalize a controllable text summarization task and categorize controllability attributes according to their shared characteristics and objectives. |
| Outcome: | The proposed method is tailored to meet the specific intent and needs of users. |
Chart-to-Text: A Large-Scale Benchmark for Chart Summarization (2022.acl-long)
Copied to clipboard
Shankar Kantharaj, Rixie Tiffany Leong, Xiang Lin, Ahmed Masry, Megh Thakkar, Enamul Hoque, Shafiq Joty
| Challenge: | Inferring key insights from charts can be challenging and time-consuming. |
| Approach: | They propose a task where the goal is to explain a chart and summarize key takeaways from it in natural language. |
| Outcome: | The proposed model produces fluent summaries but suffers from hallucinations and factual errors . the proposed model is compared with other models and can be used to generate BLEU scores . |
Textomics: A Dataset for Genomics Data Summary Generation (2022.acl-long)
Copied to clipboard
| Challenge: | Summarizing biomedical discovery from genomics data is done manually but is slowing down the progress of scientific discovery. |
| Approach: | They propose a novel task of generating sentences to summarize a genomics data matrix using neural text generation. |
| Outcome: | The proposed model improves on the previous models and can be applied to other biomedical and natural language processing applications. |
Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction (2021.acl-long)
Copied to clipboard
| Challenge: | Existing methods to extract event records from text decompose complex structure prediction task into multiple subtasks. |
| Approach: | They propose a sequence-to-structure generation paradigm that can extract events from text . they propose unified event extraction, constrained decoding algorithm and curriculum learning algorithm . |
| Outcome: | The proposed method can achieve competitive performance using record-level annotations in both supervised learning and transfer learning settings. |
The State and Fate of Summarization Datasets: A Survey (2025.naacl-long)
Copied to clipboard
| Challenge: | Summarization is the task of shortening a text while preserving the most important information it contains. |
| Approach: | They propose a novel ontology covering sample properties, collection methods and distribution covering sample characteristics, collection method and distribution. |
| Outcome: | The proposed ontology covers sample properties, collection methods and distribution, and can be used to streamline future research into a more coherent body of work. |
Enhanced Transformer Model for Data-to-Text Generation (D19-56)
Copied to clipboard
| Challenge: | Neural models have shown significant progress on data-to-text generation tasks . data- to-text models generate descriptive texts from non-linguistic structured data . |
| Approach: | They propose a new data-to-text generation model which learns content selection and summary generation in an end-to end fashion. |
| Outcome: | The proposed model outperforms current state-of-the-art models on content selection precision and content ordering metrics. |
WikiTableT: A Large-Scale Data-to-Text Dataset for Generating Wikipedia Article Sections (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing datasets for data-to-text generation focus on single-sentence generation or long-form generation. |
| Approach: | They create a dataset that pairs Wikipedia sections with tabular data and various metadata. |
| Outcome: | The proposed dataset can generate fluent and high quality texts but struggle with coherence and factuality. |