In this study, we introduce a novel cover image generation task that produces both a concise summary and a visually corresponding image from a text-only document. Because no existing datasets are available for this task, we propose a multimodal pseudo-labeling method to construct high-quality datasets at low cost. We first collect documents with summaries, multiple images, and captions, and then exclude factually inconsistent instances. Our approach selects one image from multiple images accompanying each document. Using the gold summary, we independently rank both the images and their captions. Then, we annotate a pseudo-label for an image when both the image and its corresponding caption are ranked first in their respective rankings. Finally, we remove documents that contain direct image references within texts. Experimental results demonstrate that the proposed multimodal pseudo-labeling method constructs more precise datasets and generates higher quality images than text- and image-only pseudo-labeling methods, which consider captions and images separately.

Contrastive learning (CL) has achieved remarkable progress in natural language processing (NLP), primarily as a paradigm for pre-training and fine-tuning. However, its potential during the generation phase, particularly in in-context learning (ICL)-based retrieval-augmented summarization, remains largely unexplored. While previous studies have attempted to incorporate negative samples into ICL prompts, these methods do not enforce a true contrastive objective that encourages separation of positive and negative samples in the representation space. In this paper, we first demonstrate through preliminary experiments that small language models (SLMs) can interpret contrastive prompts and effectively distinguish between positive and negative samples during inference, without any parameter updates. Building on these findings, we propose ConRAS, a novel framework that injects contrastive objectives into ICL-based retrieval-augmented summarization. Extensive experiments and in-depth analysis on three summarization benchmarks using four SLMs show that ConRAS consistently outperforms state-of-the-art retrieval-augmented methods, achieving significant improvements in summary quality.

Ranking is a fundamental component in a wide range of AI applications. However, large language models (LLMs) remain unstable on long-context ranking. Sliding-window processing is costly and listwise prompting over full candidates still yields inconsistent orders. We show that sampling alone, even with selection-based methods, cannot stabilize ranking because LLM consistency decomposes into within-list order and cross-list preference, in which a single stochastic process cannot align. To address this, we introduce Self-Sorting (SS), which generates m candidate lists and performs n selection-time re-rankings over those lists. SS fuses explicit within-list positions with implicit cross-list preferences to score entities and return a top-k set. Experimental results on five widely used ranking benchmarks show significant improvements in nDCG@1,5,10, highlighting the critical role of implicit consistency.