LLMs are applied in a generative pipeline for extracting, normalizing, and interpreting eligibility criteria from securities prospectuses, achieving up to 91% precision in document-level decisions with a conservative bias.
Deep Video Understanding through Summarization
4 Pith papers cite this work, alongside 32 external citations. Polarity classification is still indexing.
4
Pith papers citing it
32
external citations · external index
years
2026 4verdicts
UNVERDICTED 4representative citing papers
StoryLens creates a benchmark and models for context-enriched story rewriting that better matches reader preferences than style transfer alone.
QEVA is a new reference-free evaluation metric for narrative video summarization that uses multimodal question answering to measure coverage, factuality, and chronology, achieving higher correlation with human judgments than prior methods on the introduced MLVU(VS)-Eval benchmark.
Fine-tuned multilingual LLMs achieve top shared-task scores on financial causality extraction in English and Spanish.
citing papers explorer
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LLM-Based Examination of Eligibility Criteria from Securities Prospectuses at the German Central Bank
LLMs are applied in a generative pipeline for extracting, normalizing, and interpreting eligibility criteria from securities prospectuses, achieving up to 91% precision in document-level decisions with a conservative bias.
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StoryLens: Preference-Aligned Story Rewriting via Context-Aware Narrative Enrichment
StoryLens creates a benchmark and models for context-enriched story rewriting that better matches reader preferences than style transfer alone.
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QEVA: A Reference-Free Evaluation Metric for Narrative Video Summarization with Multimodal Question Answering
QEVA is a new reference-free evaluation metric for narrative video summarization that uses multimodal question answering to measure coverage, factuality, and chronology, achieving higher correlation with human judgments than prior methods on the introduced MLVU(VS)-Eval benchmark.
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Causal Connections: Leveraging Multilingual Fine-Tuning for Financial QA@FinCausal 2026
Fine-tuned multilingual LLMs achieve top shared-task scores on financial causality extraction in English and Spanish.