# Accelerating Scientific Discovery with Multi-Document Summarization of Impact-Ranked Papers

arXiv: https://arxiv.org/abs/2508.03962 · HTML: https://mlsanity.com/paper/2508.03962/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 14 references that are in the map.
- First posted on arXiv 5 Aug 2025.

## Builds on (most cited first, 10 of 10)

- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [LLM4SR: A Survey on Large Language Models for Scientific Research](https://mlsanity.com/paper/2501.04306/) (2025)
- [Scientific Article Summarization Using Citation-Context and Article's Discourse Structure](https://mlsanity.com/paper/1704.06619/) (2017)
- [Generating a Structured Summary of Numerous Academic Papers: Dataset and Method](https://mlsanity.com/paper/2302.04580/) (2023)
- [LitLLMs, LLMs for Literature Review: Are we there yet?](https://mlsanity.com/paper/2412.15249/) (2024)
- [CORE-GPT: Combining Open Access research and large language models for credible, trustworthy question answering](https://mlsanity.com/paper/2307.04683/) (2023)
- [AI and Generative AI for Research Discovery and Summarization](https://mlsanity.com/paper/2401.06795/) (2024)
- [CS-PaperSum: A Large-Scale Dataset of AI-Generated Summaries for Scientific Papers](https://mlsanity.com/paper/2502.20582/) (2025)
- [TL;DR Progress: Multi-faceted Literature Exploration in Text Summarization](https://mlsanity.com/paper/2402.06913/) (2024)
- [From Keywords to Structured Summaries: Streamlining Scholarly Information Access](https://mlsanity.com/paper/2402.14622/) (2024)

## Built on by (most cited first, 1 of 1)

- [HALO: Interactive Co-abductive Reasoning in Scientific Hypothesis Generation](https://mlsanity.com/paper/2607.18564/) (2026)

## Abstract (arXiv)

The growing volume of scientific literature makes it challenging for scientists to move from a list of papers to a synthesized understanding of a topic. Because of the constant influx of new papers on a daily basis, even if a scientist identifies a promising set of papers, they still face the tedious task of individually reading through dozens of titles and abstracts to make sense of occasionally conflicting findings. To address this critical bottleneck in the research workflow, we introduce a summarization feature to BIP! Finder, a scholarly search engine that ranks literature based on distinct impact aspects like popularity and influence. Our approach enables users to generate two types of summaries from top-ranked search results: a concise summary for an instantaneous at-a-glance comprehension and a more comprehensive literature review-style summary for greater, better-organized comprehension. This ability dynamically leverages BIP! Finder's already existing impact-based ranking and filtering features to generate context-sensitive, synthesized narratives that can significantly accelerate literature discovery and comprehension.