@inproceedings{wan-etal-2026-locate,
    title = "Locate and Explain: Joint Multimodal Emotion Cause Extraction and Summarization in Conversation",
    author = "Wan, Jikun  and
      Gong, Chen  and
      Fu, Guohong",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.2012/",
    pages = "43472--43489",
    ISBN = "979-8-89176-390-6",
    abstract = "Multimodal emotion cause analysis in conversation aims to identify the causes of emotions by leveraging multimodal information. Existing studies mainly formulate this problem as either utterance-level emotion cause extraction, which provides clear cause localization but limited explanation, or multimodal emotion cause generation, which offers fine-grained explanations but lacks explicit traceability to source utterances. Moreover, existing datasets rely heavily on human judgment and lack well-defined structured theoretical guidance, leading to subjective and inconsistent annotations. To address these issues, we introduce joint Multimodal Emotion Cause Extraction and Summarization in conversation (MECES), a new task that simultaneously extracts emotion cause utterances and generates cause summaries, enabling both precise localization and interpretable explanations of emotion cause. We further construct a MECES dataset guided by the Activating Events{--}Beliefs{--}Consequences theory from psychology. This dataset consists of 5,787 emotion utterances annotated with causes, comprising 12,231 emotion-cause pairs and 6,040 cause summaries. We also propose an effective end-to-end joint learning approach for MECES task, establishing strong benchmark results for this newly introduced task and dataset."
}