arXiv AI
Aug 6
MIDAS: Multi-LLM Iterative Data-Adaptive Summarization
arXiv:2608. 04307v1 Announce Type: cross Abstract: Text summarization is deceptively difficult.
CARPAS: Towards Content-Aware Refinement of Provided Aspects for Summarization in Large Language Models
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arXiv AI
Jun 2
Understanding LLM Behavior in Multi-Target Cross-Lingual Summarization
arXiv:2606. 01252v1 Announce Type: cross Abstract: Multi-target cross-lingual text summarization (MTXLS), which summarizes a source document into multiple target languages, is increasingly important as users consume content in diverse languages, but remains underexplored.
arXiv AI
Aug 5
Decoupling Generation and Selection for Budget-Constrained Faithful Summarization
arXiv:2608. 03655v1 Announce Type: cross Abstract: Abstractive summarization models remain vulnerable to factual inconsistency, redundancy, and weak length control.
arXiv AI
Jun 12
ReSum: Synergizing LLM Reasoning and Summarization with Reinforcement Learning
arXiv:2606. 13316v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) is a central technique for improving long-horizon reasoning in Large Language Models (LLMs).
arXiv AI
Jun 6
IDEAL: Leveraging Infinite and Dynamic Characterizations of Large Language Models for Query-focused Summarization
arXiv:2407. 10486v3 Announce Type: replace Abstract: Query-focused summarization (QFS) aims to produce summaries that answer particular questions of interest, enabling greater user control and personalization.
arXiv AI
Jun 3
A Training-Free Mixture-of-Agents Framework for Multi-Document Summarization using LLMs and Knowledge Graphs
arXiv:2606. 03867v1 Announce Type: cross Abstract: Multi-Document Summarization (MDS) plays a critical role in distilling essential information from collections of textual data.
arXiv AI
Jul 21
SALT: Salience-Aware Lexical Trie for Long-Context Compression
arXiv:2607. 17486v1 Announce Type: cross Abstract: As large language models (LLMs) process increasingly longer prompts, computation and KV-cache memory costs have emerged as major bottlenecks in inference systems.
arXiv AI
2d ago
Value Over Language Model: Detecting Original Contribution in Writing
The paper introduces VOLM, a framework that quantifies how much original value a human adds to a document beyond what a language model could generate from a task description alone. Unlike existing tools that focus on stylistic detection, VOLM extracts content at varying granularities, reconstructs it with an LLM, and compares these reconstructions to those derived from the task description. Evaluations across news articles, ICLR peer reviews, and argumentative essays show that VOLM can distinguish human-authored texts from LLM-generated ones while remaining robust to content-preserving transformations.
arXiv AI
Jun 9
Summarization is Not Dead Yet
arXiv:2606. 08000v1 Announce Type: cross Abstract: The progress of large language models (LLMs) has fueled claims that model-generated summaries rival or even surpass human-written references, raising questions about whether summarization remains an open research problem.
arXiv AI
Aug 24
SCOPE: A Generative Approach for LLM Prompt Compression
SCOPE is a training‑free generative prompt‑compression framework that reduces LLM input length by chunking a prompt into semantically coherent segments, rewriting each chunk to be more concise, and then reconstructing a coherent prompt. Unlike token‑removal methods, SCOPE’s chunk‑level rewriting preserves critical information and text coherence, and includes optimization techniques for finer‑grained control of compression ratios. Extensive evaluations on question‑answering and summarization tasks show that SCOPE consistently outperforms selective compression baselines, especially at high compression ratios.
arXiv AI
Jul 24
Reexamining zero-shot summarization: Empirical investigation of trustworthiness of LLM-summarizers
arXiv:2607. 21010v1 Announce Type: new Abstract: Zero-shot summarization using Large Language Models (LLMs) has significantly advanced the abstractive summarization task by producing coherent and fluent summaries.
arXiv AI
Aug 19
Attention Flows: Tracing LLM Conceptual Engagement via Story Summaries
The paper investigates how large language models (LLMs) engage with long-form narratives by comparing their generated novel summaries to human-authored ones. Researchers align sentences from 150 human-written summaries to specific chapters, highlighting the challenge of this alignment task and the complexity of summarization. They find stylistic differences and that LLMs tend to focus more on the ends of texts, suggesting insights into why models may struggle with narrative comprehension.