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Found 14 papers, 6 papers with code
Multi-Stage Pre-training Enhanced by ChatGPT for Multi-Scenario Multi-Domain Dialogue Summarization
Specifically, we first conduct domain-aware pre-training using large-scale multi-scenario multi-domain dialogue data to enhance the adaptability of our pre-trained model.
CFSum: A Coarse-to-Fine Contribution Network for Multimodal Summarization
Therefore, we propose a novel Coarse-to-Fine contribution network for multimodal Summarization (CFSum) to consider different contributions of images for summarization.
Life-long Learning for Multilingual Neural Machine Translation with Knowledge Distillation
To alleviate the CF, we investigate knowledge distillation based life-long learning methods.
Other Roles Matter! Enhancing Role-Oriented Dialogue Summarization via Role Interactions
Therefore, we propose a novel role interaction enhanced method for role-oriented dialogue summarization.
CSDS: A Fine-Grained Chinese Dataset for Customer Service Dialogue Summarization
Therefore, in this paper, we introduce a novel Chinese dataset for Customer Service Dialogue Summarization (CSDS).
Learning N:M Fine-grained Structured Sparse Neural Networks From Scratch
In this paper, we are the first to study training from scratch an N:M fine-grained structured sparse network, which can maintain the advantages of both unstructured fine-grained sparsity and structured coarse-grained sparsity simultaneously on specifically designed GPUs.
Knowledge Graph Enhanced Neural Machine Translation via Multi-task Learning on Sub-entity Granularity
Previous studies combining knowledge graph (KG) with neural machine translation (NMT) have two problems: i) Knowledge under-utilization: they only focus on the entities that appear in both KG and training sentence pairs, making much knowledge in KG unable to be fully utilized.
Multimodal Sentence Summarization via Multimodal Selective Encoding
Thus, we propose a multimodal selective gate network that considers reciprocal relationships between textual and multi-level visual features, including global image descriptor, activation grids, and object proposals, to select highlights of the event when encoding the source sentence.
Bridging the Modality Gap for Speech-to-Text Translation
End-to-end speech translation aims to translate speech in one language into text in another language via an end-to-end way.
Attend, Translate and Summarize: An Efficient Method for Neural Cross-Lingual Summarization
Cross-lingual summarization aims at summarizing a document in one language (e. g., Chinese) into another language (e. g., English).
NCLS: Neural Cross-Lingual Summarization
Moreover, we propose to further improve NCLS by incorporating two related tasks, monolingual summarization and machine translation, into the training process of CLS under multi-task learning.
MSMO: Multimodal Summarization with Multimodal Output
In this paper, we propose a novel task, multimodal summarization with multimodal output (MSMO).
Ensure the Correctness of the Summary: Incorporate Entailment Knowledge into Abstractive Sentence Summarization
In this paper, we investigate the sentence summarization task that produces a summary from a source sentence.
Ranked #7 on
Text Summarization
on DUC 2004 Task 1
Multi-modal Summarization for Asynchronous Collection of Text, Image, Audio and Video
In this work, we propose an extractive Multi-modal Summarization (MMS) method which can automatically generate a textual summary given a set of documents, images, audios and videos related to a specific topic.
Automatic Speech Recognition (ASR)
Document Summarization
+1
