# Neural Wikipedian: Generating Textual Summaries from Knowledge Base Triples

arXiv: https://arxiv.org/abs/1711.00155 · HTML: https://mlsanity.com/paper/1711.00155/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2017.

## Built on by (most cited first, 13 of 13)

- [Structural Adapters in Pretrained Language Models for AMR-to-text Generation](https://mlsanity.com/paper/2103.09120/) (2021)
- [Does Pretraining for Summarization Require Knowledge Transfer?](https://mlsanity.com/paper/2109.04953/) (2021)
- [Open-world Story Generation with Structured Knowledge Enhancement: A Comprehensive Survey](https://mlsanity.com/paper/2212.04634/) (2022)
- [Generating Full Length Wikipedia Biographies: The Impact of Gender Bias on the Retrieval-Based Generation of Women Biographies](https://mlsanity.com/paper/2204.05879/) (2022)
- [WDV: A Broad Data Verbalisation Dataset Built from Wikidata](https://mlsanity.com/paper/2205.02627/) (2022)
- [Descartes: Generating Short Descriptions of Wikipedia Articles](https://mlsanity.com/paper/2205.10012/) (2022)
- [A Single Example Can Improve Zero-Shot Data Generation](https://mlsanity.com/paper/2108.06991/) (2021)
- [XF2T: Cross-lingual Fact-to-Text Generation for Low-Resource Languages](https://mlsanity.com/paper/2209.11252/) (2022)
- [XWikiGen: Cross-lingual Summarization for Encyclopedic Text Generation in Low Resource Languages](https://mlsanity.com/paper/2303.12308/) (2023)
- [Mitigating Cognitive Biases in Multi-Criteria Crowd Assessment](https://mlsanity.com/paper/2407.18938/) (2024)
- [Tgea: An error-annotated dataset and benchmark tasks for text generation from pretrained language models](https://mlsanity.com/paper/2503.04232/) (2025)
- [Mapping Process for the Task: Wikidata Statements to Text as Wikipedia Sentences](https://mlsanity.com/paper/2210.12659/) (2022)
- [Cross-lingual Biography Enrichment via Claim Extraction and Alignment](https://mlsanity.com/paper/2608.23390/) (2026)

## Abstract (arXiv)

Most people do not interact with Semantic Web data directly. Unless they have the expertise to understand the underlying technology, they need textual or visual interfaces to help them make sense of it. We explore the problem of generating natural language summaries for Semantic Web data. This is non-trivial, especially in an open-domain context. To address this problem, we explore the use of neural networks. Our system encodes the information from a set of triples into a vector of fixed dimensionality and generates a textual summary by conditioning the output on the encoded vector. We train and evaluate our models on two corpora of loosely aligned Wikipedia snippets and DBpedia and Wikidata triples with promising results.