# Combining Deep Neural Reranking and Unsupervised Extraction for Multi-Query Focused Summarization

arXiv: https://arxiv.org/abs/2302.01148 · HTML: https://mlsanity.com/paper/2302.01148/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 36 references that are in the map.
- First posted on arXiv 2 Feb 2023.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT](https://mlsanity.com/paper/2004.12832/) (2020)
- [ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction](https://mlsanity.com/paper/2112.01488/) (2021)
- [RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2010.08191/) (2020)
- [Pretrained Transformers for Text Ranking: BERT and Beyond](https://mlsanity.com/paper/2010.06467/) (2020)
- [Document Expansion by Query Prediction](https://mlsanity.com/paper/1904.08375/) (2019)
- [TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection](https://mlsanity.com/paper/1911.04118/) (2019)
- [Multi-document Summarization via Deep Learning Techniques: A Survey](https://mlsanity.com/paper/2011.04843/) (2020)
- [ASPECTNEWS: Aspect-Oriented Summarization of News Documents](https://mlsanity.com/paper/2110.08296/) (2021)
- [Declarative Experimentation in Information Retrieval using PyTerrier](https://mlsanity.com/paper/2007.14271/) (2020)
- [Generating Query Focused Summaries from Query-Free Resources](https://mlsanity.com/paper/2012.14774/) (2020)
- [Adaptive Re-Ranking with a Corpus Graph](https://mlsanity.com/paper/2208.08942/) (2022)
- [Enhancing Crisis-Related Tweet Classification with Entity-Masked Language Modeling and Multi-Task Learning](https://mlsanity.com/paper/2211.11468/) (2022)

## Built on by (most cited first, 2 of 2)

- [Multi-Query Focused Disaster Summarization via Instruction-Based Prompting](https://mlsanity.com/paper/2402.09008/) (2024)
- [DQNC2S: DQN-based Cross-stream Crisis event Summarizer](https://mlsanity.com/paper/2401.06683/) (2024)

## Abstract (arXiv)

The CrisisFACTS Track aims to tackle challenges such as multi-stream fact-finding in the domain of event tracking; participants' systems extract important facts from several disaster-related events while incorporating the temporal order. We propose a combination of retrieval, reranking, and the well-known Integer Linear Programming (ILP) and Maximal Marginal Relevance (MMR) frameworks. In the former two modules, we explore various methods including an entity-based baseline, pre-trained and fine-tuned Question Answering systems, and ColBERT. We then use the latter module as an extractive summarization component by taking diversity and novelty criteria into account. The automatic scoring runs show strong results across the evaluation setups but also reveal shortcomings and challenges.