Entity disambiguation links mentions in text to the entities they refer to in a knowledge base. Recent cross-entity interaction models improve candidate comparison by jointly encoding multiple candidates, but incorporating entity metadata into this joint encoding is not straightforward. Appending metadata to every candidate forces joint encoding to process longer inputs with detailed distractor information, which can weaken the contrastive cues that make cross-entity interaction effective. We propose DuCEI, a dual-stage framework that avoids this broad metadata interaction by reducing the candidate set before introducing entity metadata. DuCEI first compares all candidates using title-based cross-entity interaction, and then performs metadata-enriched interaction only among the remaining candidates. This design makes metadata use selective at the candidate level, applying detailed evidence only where it is likely to help. Experiments on six standard benchmarks show that DuCEI achieves the best overall performance among models trained solely on AIDA-CoNLL. Further analyses show that confidence-based selective refinement preserves nearly all of the full two-stage performance while applying Stage 2 to only 27.80% of mentions, reducing inference cost from 1.90× to approximately 1.27× that of Stage 1 alone. Our code is available at https://github.com/jinwoomin/DuCEI.

Large language models (LLMs) trained on massive text corpora may inadvertently memorize sensitive or copyrighted content, motivating the need for more targeted unlearning. Selective LLM unlearning focuses on identifying token-level or span-level unlearning targets within a text, rather than treating entire sequences as unlearning targets. However, many existing selective approaches depend on external supervision to identify unlearning targets, which may misalign unlearning objectives with the model’s internal behavior. In this paper, we propose a selective span-level unlearning method that is grounded entirely in model-intrinsic information. Our method first estimates token-level importance scores by contrasting gradient information induced by forget and retain datasets, identifying tokens that disproportionately contribute to information targeted for unlearning. These token-level importance scores are then used as anchors to identify coherent span-level unlearning targets via a self-consistency–based generation process, allowing the model to determine stable spans based on its own predictions. Experiments on two LLM unlearning benchmarks show that our approach achieves comparable unlearning performance while substantially better preserving retained knowledge.

Multi-stage long document summarization, which splits a long document as multiple segments and each of which is used to generate a coarse summary in multiple stage, and then the final summary is produced using the last coarse summary, is a flexible approach to capture salient information from the long document. Even if the coarse summary affects the final summary, however, the coarse summarizer in the existing multi-stage summarization is coarsely trained using data segments that are not useful to generate the final summary. In this paper, we propose a novel method for multi-stage long document summarization. The proposed method first generates new segment pairs, ensuring that all of them are relevant to generating the final summary. We then incorporate contrastive learning into the training of the coarse summarizer, which tries to maximize the similarities between source segments and the target summary during training. Through extensive experiments on six long document summarization datasets, we demonstrate that our proposed method not only enhances the existing multi-stage long document summarization approach, but also achieves performance comparable to state-of-the-art methods, including those utilizing large language models for long document summarization.

Korean morphological analysis has been considered as a sequence of morpheme processing and POS tagging. Thus, a pipeline model of the tasks has been adopted widely by previous studies. However, the model has a problem that it cannot utilize interactions among the tasks. This paper formulates Korean morphological analysis as a combination of the tasks and presents a tied sequence-to-sequence multi-task model for training the two tasks simultaneously without any explicit regularization. The experiments prove the proposed model achieves the state-of-the-art performance.