# A Mixed Hierarchical Attention based Encoder-Decoder Approach for Standard Table Summarization

arXiv: https://arxiv.org/abs/1804.07790 · HTML: https://mlsanity.com/paper/1804.07790/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2018.

## Built on by (most cited first, 5 of 5)

- [Robust (Controlled) Table-to-Text Generation with Structure-Aware Equivariance Learning](https://mlsanity.com/paper/2205.03972/) (2022)
- [MultiTabQA: Generating Tabular Answers for Multi-Table Question Answering](https://mlsanity.com/paper/2305.12820/) (2023)
- [Stylized Data-to-Text Generation: A Case Study in the E-Commerce Domain](https://mlsanity.com/paper/2305.03256/) (2023)
- [QFMTS: Generating Query-Focused Summaries over Multi-Table Inputs](https://mlsanity.com/paper/2405.05109/) (2024)
- [Few-Shot Data-to-Text Generation via Unified Representation and Multi-Source Learning](https://mlsanity.com/paper/2308.05317/) (2023)

## Abstract (arXiv)

Structured data summarization involves generation of natural language summaries from structured input data. In this work, we consider summarizing structured data occurring in the form of tables as they are prevalent across a wide variety of domains. We formulate the standard table summarization problem, which deals with tables conforming to a single predefined schema. To this end, we propose a mixed hierarchical attention based encoder-decoder model which is able to leverage the structure in addition to the content of the tables. Our experiments on the publicly available WEATHERGOV dataset show around 18 BLEU (~ 30%) improvement over the current state-of-the-art.