arXiv AI
Aug 6
MIDAS: Multi-LLM Iterative Data-Adaptive Summarization
arXiv:2608. 04307v1 Announce Type: cross Abstract: Text summarization is deceptively difficult.
Segment-level Tree Search for Long Meeting Document Summarization
arXiv:2606. 08445v1 Announce Type: cross Abstract: Meeting documents are challenging to summarize due to their length and complex conversational structure.
Related stories
Hugging Face Trending Papers
Jun 23
Less is More: Quality-Aware Training Data Selection for Scientific Summarization
Scientific long-document summarization datasets commonly treat author-written abstracts as gold reference summaries, although their quality and alignment with the source article vary. At the same time, publicly available scientific summarization datasets remain limited in scale and structure for modern long-context models.
arXiv Computation and Language
Sep 3
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.
arXiv Computation and Language
Sep 22
Speech-to-SOAP: End-to-End Summarization of Medical Dialogues: KIT@BeTraC 2026
arXiv:2608.24327v2 Announce Type: replace Abstract: With the advent of Large Language Models and its instruction following capabilities a promising application is the task of summarization. Within th...
arXiv AI
Jun 12
G-Long: Graph-Enhanced Memory Management for Efficient Long-Term Dialogue Agents
arXiv:2606. 13115v1 Announce Type: cross Abstract: While Large Language Models (LLMs) have advanced open-domain dialogue systems, maintaining long-term consistency remains a challenge due to inherent limitations in long-context reasoning and the inefficiency of processing extensive raw text.
arXiv AI
Jun 19
A BART-based approach with hierarchical strategy for Vietnamese abstractive multi-document summarization
arXiv:2606. 19591v1 Announce Type: cross Abstract: In this technical report, we focus on solving the challenge of Vietnamese multi-document abstractive summarization, introduced in the International Workshop on Vietnamese Language and Speech Processing (VLSP) 2022.
arXiv AI
6d ago
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
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.
OpenAI Blog
Sep 4, 2020
Learning to summarize with human feedback
We’ve applied reinforcement learning from human feedback to train language models that are better at summarization.
arXiv AI
Jun 9
DYCP: Dynamic Context Pruning for Long-Form Dialogue with LLMs
arXiv:2601. 07994v5 Announce Type: replace-cross Abstract: Large Language Models (LLMs) increasingly operate over long-form dialogues with frequent topic shifts.
arXiv Computation and Language
Sep 4
SGD-KV: Summarization Guided KV Cache Compression
SGD-KV is a head‑aware framework for compressing key‑value caches in large language models. It uses a chunk‑summarization diagnostic task to identify attention heads that specialize in hierarchical information aggregation, allowing the KV cache budget to be allocated based on each head’s summarization score. Experiments on Qwen2.5‑7B‑1M and Qwen3‑32B show state‑of‑the‑art performance on up to 1M‑token contexts while cutting KV cache memory usage by up to 75%.
Hugging Face Trending Papers
Sep 3
SGD-KV: Summarization Guided KV Cache Compression
SGD-KV is a head‑aware framework that compresses key‑value caches in large language models by using a chunk‑summarization diagnostic task to identify attention heads that specialize in hierarchical information aggregation. It prioritizes these heads during compression, achieving state‑of‑the‑art performance on long‑context benchmarks with up to 1M tokens while cutting KV cache memory usage by as much as 75%. Experiments on Qwen2.5‑7B‑1M and Qwen3‑32B confirm that allocating cache budget based on summarization scores yields a superior efficiency‑accuracy trade‑off for long‑context inference.