# Query-Focused Extractive Summarization for Sentiment Explanation

arXiv: https://arxiv.org/abs/2509.11989 · HTML: https://mlsanity.com/paper/2509.11989/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 40 references that are in the map.
- First posted on arXiv 15 Sep 2025.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization](https://mlsanity.com/paper/1808.08745/) (2018)
- [Teaching Machines to Read and Comprehend](https://mlsanity.com/paper/1506.03340/) (2015)
- [Faithful or Extractive? On Mitigating the Faithfulness-Abstractiveness Trade-off in Abstractive Summarization](https://mlsanity.com/paper/2108.13684/) (2021)
- [Diversity driven Attention Model for Query-based Abstractive Summarization](https://mlsanity.com/paper/1704.08300/) (2017)
- [BERT-QE: Contextualized Query Expansion for Document Re-ranking](https://mlsanity.com/paper/2009.07258/) (2020)
- [Correcting Diverse Factual Errors in Abstractive Summarization via Post-Editing and Language Model Infilling](https://mlsanity.com/paper/2210.12378/) (2022)
- [Generating Query Focused Summaries from Query-Free Resources](https://mlsanity.com/paper/2012.14774/) (2020)
- [WSL-DS: Weakly Supervised Learning with Distant Supervision for Query Focused Multi-Document Abstractive Summarization](https://mlsanity.com/paper/2011.01421/) (2020)
- [Biased TextRank: Unsupervised Graph-Based Content Extraction](https://mlsanity.com/paper/2011.01026/) (2020)
- [A Two-Stage Masked LM Method for Term Set Expansion](https://mlsanity.com/paper/2005.01063/) (2020)
- [What Makes a Good and Useful Summary? Incorporating Users in Automatic Summarization Research](https://mlsanity.com/paper/2012.07619/) (2020)

## Abstract (arXiv)

Constructive analysis of feedback from clients often requires determining the cause of their sentiment from a substantial amount of text documents. To assist and improve the productivity of such endeavors, we leverage the task of Query-Focused Summarization (QFS). Models of this task are often impeded by the linguistic dissonance between the query and the source documents. We propose and substantiate a multi-bias framework to help bridge this gap at a domain-agnostic, generic level; we then formulate specialized approaches for the problem of sentiment explanation through sentiment-based biases and query expansion. We achieve experimental results outperforming baseline models on a real-world proprietary sentiment-aware QFS dataset.