1 |
Rewards with Negative Examples for Reinforced Topic-Focused Abstractive Summarization ...
|
|
|
|
BASE
|
|
Show details
|
|
2 |
Low-Resource Dialogue Summarization with Domain-Agnostic Multi-Source Pretraining ...
|
|
|
|
BASE
|
|
Show details
|
|
3 |
HETFORMER: Heterogeneous Transformer with Sparse Attention for Long-Text Extractive Summarization ...
|
|
|
|
BASE
|
|
Show details
|
|
4 |
CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization ...
|
|
|
|
BASE
|
|
Show details
|
|
5 |
Weakly supervised discourse segmentation for multiparty oral conversations ...
|
|
|
|
Abstract:
Anthology paper link: https://aclanthology.org/2021.emnlp-main.104/ Abstract: Discourse segmentation, the first step of discourse analysis, has been shown to improve results for text summarization, translation and other NLP tasks. While segmentation models for written text tend to perform well, they are not directly applicable to spontaneous, oral conversation, which has linguistic features foreign to written text. Segmentation is less studied for this type of language, where annotated data is scarce, and existing corpora more heterogeneous. We develop a weak supervision approach to adapt, using minimal annotation, a state of the art discourse segmenter trained on written text to French conversation transcripts. Supervision is given by a latent model bootstrapped by manually defined heuristic rules that use linguistic and acoustic information. The resulting model improves the original segmenter, especially in contexts where information on speaker turns is lacking or noisy, gaining up to 13% in F-score. ...
|
|
Keyword:
Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing; Text Summarization
|
|
URL: https://underline.io/lecture/37794-weakly-supervised-discourse-segmentation-for-multiparty-oral-conversations https://dx.doi.org/10.48448/qyxq-tq15
|
|
BASE
|
|
Hide details
|
|
6 |
Vision Guided Generative Pre-trained Language Models for Multimodal Abstractive Summarization ...
|
|
|
|
BASE
|
|
Show details
|
|
7 |
Evaluation of Summarization Systems across Gender, Age, and Race ...
|
|
|
|
BASE
|
|
Show details
|
|
8 |
Controllable Neural Dialogue Summarization with Personal Named Entity Planning ...
|
|
|
|
BASE
|
|
Show details
|
|
9 |
CSDS: A Fine-Grained Chinese Dataset for Customer Service Dialogue Summarization ...
|
|
|
|
BASE
|
|
Show details
|
|
10 |
A Thorough Evaluation of Task-Specific Pretraining for Summarization ...
|
|
|
|
BASE
|
|
Show details
|
|
12 |
Context or No Context? A preliminary exploration of human-in-the-loop approach for Incremental Temporal Summarization in meetings ...
|
|
|
|
BASE
|
|
Show details
|
|
13 |
Exploring Multitask Learning for Low-Resource Abstractive Summarization ...
|
|
|
|
BASE
|
|
Show details
|
|
14 |
Capturing Speaker Incorrectness: Speaker-Focused Post-Correction for Abstractive Dialogue Summarization ...
|
|
|
|
BASE
|
|
Show details
|
|
15 |
Narrative Embedding: Re-Contextualization Through Attention ...
|
|
|
|
BASE
|
|
Show details
|
|
16 |
TWEETSUMM - A Dialog Summarization Dataset for Customer Service ...
|
|
|
|
BASE
|
|
Show details
|
|
17 |
SUBSUME: A Dataset for Subjective Summary Extraction from Wikipedia Documents ...
|
|
|
|
BASE
|
|
Show details
|
|
18 |
AUTOSUMM: Automatic Model Creation for Text Summarization ...
|
|
|
|
BASE
|
|
Show details
|
|
19 |
A Statistical Analysis of Summarization Evaluation Metrics Using Resampling Methods ...
|
|
|
|
BASE
|
|
Show details
|
|
|
|