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
The paper introduces CARPAS, a new task that dynamically refines user-provided aspects for aspect-based summarization in large language models (LLMs). It presents three new datasets and evaluates four prompting strategies, finding that LLMs tend to over-generate aspects, leading to overly long and misaligned summaries. To address this, the authors propose a two-stage framework that first generates lightweight scope guidance before aspect refinement and summarization, which improves focus, reduces over-generation, and enhances performance across all datasets.
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arXiv AI
Sep 28
Highlight-Then-Summarize: Learning to Compress Evidence for Long-Context Understanding
The paper introduces Highlight-Then-Summarize (H2S), a two-step approach that first highlights question-relevant evidence in long documents and then condenses it into a compact, question-conditioned summary before generating an answer. The authors built the H2S-Dataset with 6,647 examples spanning 11 benchmark families, and developed H2S-RL to reward evidence selection and summary construction. Evaluated on the H2S-Bench suite, the H2S-14B model outperforms larger open-source models, achieving the highest overall score and maintaining strong performance even with a reduced output budget.
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
Sep 2
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
4d ago
Towards robust long-context understanding of large language model via active recap learning
arXiv:2601.13734v2 Announce Type: replace-cross Abstract: In this paper, we propose active recap learning (ARL), a framework for enhancing large language model (LLM) in understanding long contexts. A...
arXiv Computation and Language
Oct 12
OARelatedWork: A Large-Scale Dataset of Related Work Sections with Full-texts from Open Access Sources
OARelatedWork is a large‑scale dataset for generating entire related‑work sections from open‑access papers, comprising 94,450 papers across multiple domains. It includes full texts of cited works and is designed so every source paper is available in full, enabling controlled evaluation of full‑text related‑work generation. The paper benchmarks various models, shows that large language models struggle with massive full‑text contexts, and introduces a statement‑level evaluation framework to better assess long‑form structured outputs.
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.