# Contextualizing Citations for Scientific Summarization using Word Embeddings and Domain Knowledge

arXiv: https://arxiv.org/abs/1705.08063 · HTML: https://mlsanity.com/paper/1705.08063/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2017.

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

- [RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval](https://mlsanity.com/paper/2401.18059/) (2024)
- [A Question Answering Framework for Decontextualizing User-facing Snippets from Scientific Documents](https://mlsanity.com/paper/2305.14772/) (2023)
- [Citance-Contextualized Summarization of Scientific Papers](https://mlsanity.com/paper/2311.02408/) (2023)
- [Inline Citation Classification using Peripheral Context and Time-evolving Augmentation](https://mlsanity.com/paper/2303.00344/) (2023)
- [Prompting LLMs with content plans to enhance the summarization of scientific articles](https://mlsanity.com/paper/2312.08282/) (2023)

## Abstract (arXiv)

Citation texts are sometimes not very informative or in some cases inaccurate by themselves; they need the appropriate context from the referenced paper to reflect its exact contributions. To address this problem, we propose an unsupervised model that uses distributed representation of words as well as domain knowledge to extract the appropriate context from the reference paper. Evaluation results show the effectiveness of our model by significantly outperforming the state-of-the-art. We furthermore demonstrate how an effective contextualization method results in improving citation-based summarization of the scientific articles.