---
title: "Challenges in Data-to-Document Generation — Wiseman 2017"
description: "Neural generators perform well on older data-to-text benchmarks such as WeatherGov, RoboCup and WikiBio."
authors: ["Sam Wiseman", "Stuart M. Shieber", "Alexander M. Rush"]
year: 2017
venue: "arXiv:1707.08052"
source: "https://arxiv.org/abs/1707.08052"
doi: "10.48550/arxiv.1707.08052"
arxiv: "1707.08052"
licence: "arXiv — non-exclusive licence to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/wiseman-2017-challenges-data-document-generation/"
---
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# Challenges in Data-to-Document Generation

**Authors:** [Sam Wiseman](https://md.research.elamur.ai/free/en/authors/sam-wiseman/), [Stuart M. Shieber](https://md.research.elamur.ai/free/en/authors/stuart-m-shieber/), [Alexander M. Rush](https://md.research.elamur.ai/free/en/authors/alexander-m-rush/) · **Year:** 2017 · **Venue:** arXiv:1707.08052 · **Licence:** [arXiv — non-exclusive licence to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/1707.08052) · [PDF](https://arxiv.org/pdf/1707.08052) · [DOI 10.48550/arxiv.1707.08052](https://doi.org/10.48550/arxiv.1707.08052) · [arXiv 1707.08052](https://arxiv.org/abs/1707.08052) · [OpenAlex](https://openalex.org/W2739046565)

**Citations:** 518 citations (openalex-snapshot · 2026-10-14) · [328](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Neural generators perform well on older data-to-text benchmarks such as WeatherGov, RoboCup and WikiBio. However, those benchmarks pair small record sets with short, simple outputs. The authors argue that a harder test is needed: producing multi-sentence documents from large structured tables. To support this, they release a corpus of NBA game write-ups aligned with box- and line-score tables, drawn from two sources, RotoWire and SBNation. Because BLEU mostly measures fluency, they also introduce an extractive evaluation. An information-extraction model reads the generated text, recovers (entity, value, type) records and compares them with the source table and the reference summary. Using this setup, they benchmark encoder-decoder models with joint or conditional copying, reconstruction losses and a template system. The neural models write fluent, stylistically convincing prose, but they often hallucinate facts, choose content poorly and lack coherent long-range structure. Templates outperform them on several content measures. Copy and reconstruction extensions help but leave a large gap to human writing. The work matters because it exposes weaknesses that surface metrics hide and offers a reusable benchmark for long-form generation.

## Key points
- **Dataset scale:** RotoWire contains 4.9K examples, about 1.6M tokens and an 11.3K vocabulary. Its summaries average 337.1 words, and each game has 628 records spanning 39 record types. SBNation is larger (10.9K examples, 8.8M tokens, 805.4-word average) but its fan-written text is far noisier. The data is hosted at github.com/harvardnlp/boxscore-data.
- **Extractive metrics:** The proposed metrics are relation generation (RG), content selection (CS) and content ordering (CO), with CO measured via DLD. They are computed from records that a relation classifier extracts from the output text.
- **Human evaluation of factual support:** Conditional Copy achieved the most box-score-supported facts per sentence (3.05). Contradicted facts were 0.70 for gold texts, compared with 1.16–2.31 for the models. Adding reconstruction plus TVD to Joint Copy significantly reduced contradictions.
- **Human evaluation of ordering:** Raters scored the naturalness of fact ordering at 5.19 for gold summaries and 3.90–4.43 for models on a 1–7 scale. These differences were not statistically significant.
- **Typical model errors:** Generated texts swapped the teams' win/loss records, produced self-contradictory statements and invented details that the tables do not support.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Other](https://md.research.elamur.ai/free/en/shelves/other/)

## Cited by

Cited by

328 papers of our library cite it (of the 140673 whose bibliographies we read); 24 of them have a page here, 304 not yet.

- [Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training](https://md.research.elamur.ai/free/en/papers/agarwal-2021-knowledge-graph-based-synthetic-corpus/)
- [Controllable Paraphrase Generation with a Syntactic Exemplar](https://md.research.elamur.ai/free/en/papers/chen-2019-controllable-paraphrase-generation-syntactic-exemplar/)
- [Logical Natural Language Generation from Open-Domain Tables](https://md.research.elamur.ai/free/en/papers/chen-2020-logical-natural-language-generation-open/)
- [A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents](https://md.research.elamur.ai/free/en/papers/cohan-2018-discourse-aware-attention-model-abstractive/)
- [FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/durmus-2020-feqa-question-answering-evaluation-framework/)
- [Semantic Noise Matters for Neural Natural Language Generation](https://md.research.elamur.ai/free/en/papers/dusek-2019-semantic-noise-matters-neural-natural/)
- [A Survey of Data Augmentation Approaches for NLP](https://md.research.elamur.ai/free/en/papers/feng-2021-survey-data-augmentation-approaches-nlp/)
- [Learning to Write with Cooperative Discriminators](https://md.research.elamur.ai/free/en/papers/holtzman-2018-learning-write-cooperative-discriminators/)
- [Survey of Hallucination in Natural Language Generation](https://md.research.elamur.ai/free/en/papers/ji-2022-survey-hallucination-natural-language-generation/)
- [Text Generation from Knowledge Graphs with Graph Transformers](https://md.research.elamur.ai/free/en/papers/kedziorski-2019-text-generation-knowledge-graphs-graph/)
- [Hurdles to Progress in Long-form Question Answering](https://md.research.elamur.ai/free/en/papers/krishna-2021-hurdles-progress-long-form-question/)
- [大语言模型的幻觉问题研究综述](https://md.research.elamur.ai/free/en/papers/liu-2024-hallucinations-large-language/)
- [Step-by-Step: Separating Planning from Realization in Neural Data-to-Text Generation](https://md.research.elamur.ai/free/en/papers/moryossef-2019-step-step-separating-planning-realization/)
- [DART: Open-Domain Structured Data Record to Text Generation](https://md.research.elamur.ai/free/en/papers/nan-2020-dart-open-domain-structured-data/)
- [ToTTo: A Controlled Table-To-Text Generation Dataset](https://md.research.elamur.ai/free/en/papers/parikh-2020-totto-controlled-table-text-generation/)
- [Measuring Attribution in Natural Language Generation Models](https://md.research.elamur.ai/free/en/papers/rashkin-2023-measuring-attribution-natural-language-generation/)
- [Investigating Pretrained Language Models for Graph-to-Text Generation](https://md.research.elamur.ai/free/en/papers/ribeiro-2021-investigating-pretrained-language-models-graph/)
- [ProgPrompt: Generating Situated Robot Task Plans using Large Language Models](https://md.research.elamur.ai/free/en/papers/singh-2022-progprompt-generating-situated-robot-task/)
- [Learning Neural Templates for Text Generation](https://md.research.elamur.ai/free/en/papers/wiseman-2018-learning-neural-templates-text-generation/)
- [On Hallucination and Predictive Uncertainty in Conditional Language Generation](https://md.research.elamur.ai/free/en/papers/xiao-2021-hallucination-predictive-uncertainty-conditional-language/)
- [CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases](https://md.research.elamur.ai/free/en/papers/yu-2019-cosql-conversational-text-sql-challenge/)
- [Big Bird: Transformers for Longer Sequences](https://md.research.elamur.ai/free/en/papers/zaheer-2020-big-bird-transformers-longer-sequences/)
- [From Graph to Text and Back: Semantic Fidelity in Automated Industrial Knowledge Graphs](https://md.research.elamur.ai/free/en/papers/zeinalipour-2026-graph-text-back-semantic-fidelity/)
- [Optimizing the Factual Correctness of a Summary: A Study of Summarizing Radiology Reports](https://md.research.elamur.ai/free/en/papers/zhang-2020-optimizing-factual-correctness-summary-study/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{wiseman2017challenges,
  title = {{Challenges in Data-to-Document Generation}},
  author = {Wiseman, Sam and Shieber, Stuart M. and Rush, Alexander M.},
  year = {2017},
  doi = {10.48550/arxiv.1707.08052},
  eprint = {1707.08052},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.1707.08052}
}
```

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