✍️ Text Generation

💬 ACL2026 · 10 paper notes

📌 Same area in other venues: 🔬 ICLR2026 (3) · 🤖 AAAI2026 (2) · 📹 ICCV2025 (1)

🔥 Top topics: Summarization ×3 · LLM ×2

Are Emotion and Rhetoric Neurons in LLM? Neuron Recognition and Adaptive Masking for Emotion-Rhetoric Prediction Steering

This paper systematically investigates the representational mechanisms of emotion and rhetoric neurons in LLMs and their intrinsic relationships. It proposes a multi-dimensional neuron recognition framework and an adaptive masking validation method, enabling targeted steering of emotion/rhetoric predictions and rhetoric-neuron-assisted emotion recognition.

ConlangCrafter: Constructing Languages with a Multi-Hop LLM Pipeline

This paper proposes ConlangCrafter, a multi-hop LLM pipeline that decomposes constructed language (conlang) design into three modular stages — phonology, grammar, and lexicon — ensuring typological diversity through randomness injection and internal consistency through self-refinement loops, along with an automatic evaluation framework incorporating typological diversity analysis and translation consistency assessment.

FACTS: Table Summarization via Offline Template Generation with Agentic Workflows

This paper proposes FACTS (Fast, Accurate, and Privacy-Compliant Table Summarization), a three-stage agentic workflow that automatically generates reusable offline templates (SQL queries + Jinja2 templates) for fast, accurate, and privacy-compliant query-focused table summarization, achieving state-of-the-art performance across FeTaQA, QTSumm, and QFMTS benchmarks.

Frankentext: Stitching Random Text Fragments into Long-Form Narratives

This paper proposes Frankentext, a paradigm where LLMs stitch random human text fragments into coherent long-form narratives under extreme constraints (90% content verbatim-copied from human writing), revealing severe failures of existing AI text detectors in mixed-authorship scenarios (72% of Frankentext is misclassified as human-written).

Losses that Cook: Topological Optimal Transport for Structured Recipe Generation

This paper proposes a topological loss function based on Sinkhorn divergence, representing ingredient lists as point clouds in embedding space and minimizing the geometric discrepancy between predicted and reference ingredients. The approach significantly improves ingredient recall and quantity precision in structured recipe generation, with generated outputs preferred by human evaluators in 62% of cases.

Planning Beyond Text: Graph-based Reasoning for Complex Narrative Generation

PLOTTER shifts narrative planning from text representation to graph structure (event graph + character graph), diagnosing and repairing narrative flaws through multi-agent Evaluate-Plan-Revise iterative cycles on graph topology, significantly outperforming existing methods on narrativity, characterization, and dramatic tension.

Right at My Level: A Unified Multilingual Framework for Proficiency-Aware Text Simplification

This paper proposes Re-RIGHT, a framework that trains a 4B policy model via GRPO with a three-module reward (vocabulary coverage + semantic preservation + coherence) to accurately simplify text in English, Japanese, Korean, and Chinese according to learner proficiency levels (CEFR/JLPT/TOPIK/HSK), outperforming large models such as GPT-5.2 and Gemini 2.5.

SCURank: Ranking Multiple Candidate Summaries with Summary Content Units for Enhanced Summarization

This paper proposes SCURank, a ranking framework based on Summary Content Units (SCUs). It extracts SCUs from candidate summaries, estimates information importance via cross-summary clustering, and scores candidates by informativeness. SCURank replaces unstable LLM-based direct ranking and coarse-grained ROUGE-based ranking. Combined with BRIO contrastive learning in a multi-LLM distillation setting, it significantly improves the summarization performance of distilled models.

ThreadSumm: Summarization of Nested Discourse Threads Using Tree of Thoughts

This paper proposes ThreadSumm, a multi-stage LLM pipeline framework that models nested discourse thread summarization as a hierarchical reasoning problem. It first extracts aspects and atomic content units (ACUs) for content planning, then constructs a thread-aware sequence via sentence ordering, and finally applies Tree of Thoughts search to generate and score multiple paragraph candidates. The approach outperforms baselines on Reddit and StackExchange datasets.

XtraGPT: Context-Aware and Controllable Academic Paper Revision via Human-AI Collaboration

This paper presents XtraGPT—the first open-source LLM suite (1.5B–14B) for academic paper revision. By fine-tuning on 7,000 top-venue papers and 140,000 criteria-guided instruction–revision pairs, it enables context-aware, paragraph-level controllable revision. The 7B variant matches GPT-4o-mini, the 14B variant surpasses it, and human evaluation shows an average predicted score improvement of 0.65 points after revision.