# SUMBot: Summarizing Context in Open-Domain Dialogue Systems

arXiv: https://arxiv.org/abs/2210.06496 · HTML: https://mlsanity.com/paper/2210.06496/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 22 references that are in the map.
- First posted on arXiv 12 Oct 2022.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://mlsanity.com/paper/1911.00536/) (2019)
- [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://mlsanity.com/paper/1801.07243/) (2018)
- [SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization](https://mlsanity.com/paper/1911.12237/) (2019)
- [AllenNLP: A Deep Semantic Natural Language Processing Platform](https://mlsanity.com/paper/1803.07640/) (2018)
- [ParlAI: A Dialog Research Software Platform](https://mlsanity.com/paper/1705.06476/) (2017)
- [ConveRT: Efficient and Accurate Conversational Representations from Transformers](https://mlsanity.com/paper/1911.03688/) (2019)
- [Do Neural Dialog Systems Use the Conversation History Effectively? An Empirical Study](https://mlsanity.com/paper/1906.01603/) (2019)
- [Pretraining Methods for Dialog Context Representation Learning](https://mlsanity.com/paper/1906.00414/) (2019)
- [A Context-aware Natural Language Generator for Dialogue Systems](https://mlsanity.com/paper/1608.07076/) (2016)
- [Neural-based Context Representation Learning for Dialog Act Classification](https://mlsanity.com/paper/1708.02561/) (2017)
- [Context Matters in Semantically Controlled Language Generation for Task-oriented Dialogue Systems](https://mlsanity.com/paper/2111.14119/) (2021)

## Abstract (arXiv)

In this paper, we investigate the problem of including relevant information as context in open-domain dialogue systems. Most models struggle to identify and incorporate important knowledge from dialogues and simply use the entire turns as context, which increases the size of the input fed to the model with unnecessary information. Additionally, due to the input size limitation of a few hundred tokens of large pre-trained models, regions of the history are not included and informative parts from the dialogue may be omitted. In order to surpass this problem, we introduce a simple method that substitutes part of the context with a summary instead of the whole history, which increases the ability of models to keep track of all the previous relevant information. We show that the inclusion of a summary may improve the answer generation task and discuss some examples to further understand the system's weaknesses.