This repository contains qualitative examples and specific details for our paper Leveraging multimodal content for podcast summarization presented at ACM SAC 2022.
The model fine-tuned to classify a given sentence as containing advertising content or not is available on 🤗 Hub. It is used as a pre-processing step to remove advertising content from text descriptions. The model is trained on a set of ~2200 manually annotated examples. The data used to train the model can be found here
If you find the resources in this repository useful for your research please cite the following paper:
@inproceedings{vaiani2022leveraging,
title={Leveraging multimodal content for podcast summarization},
author={Vaiani, Lorenzo and La Quatra, Moreno and Cagliero, Luca and Garza, Paolo},
booktitle={Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing},
pages={863--870},
year={2022}
}
Demo for the paper "Leveraging multimodal content for podcast summarization" by Lorenzo Vaiani, Moreno La Quatra, Luca Cagliero, and Paolo Garza - published in ACM SAC 2022, 2022
Audio samples for cases where MATeR generated summaries obtain significant higher ROUGE-2 F1 score if compared with the best competitor.
MATeR
7vXiAjVFnhNI3T9Gkw636a_3N0I2LsRLmalC25phOYJmh_84.700s_114s.mov
HiBERT
7vXiAjVFnhNI3T9Gkw636a_3N0I2LsRLmalC25phOYJmh_233.800s_263.400s_audio.mov
MATeR-textonly
7vXiAjVFnhNI3T9Gkw636a_3N0I2LsRLmalC25phOYJmh_60s_83.200s_audio.mov
MATeR
550ZI1sg75lbNv7TXoQbec_6VAekMPV1MjUP5k465g52D_83.300s_107.100s.mov
HiBERT
550ZI1sg75lbNv7TXoQbec_6VAekMPV1MjUP5k465g52D_30.200s_46.900s_audio.mov
MATeR-textonly
550ZI1sg75lbNv7TXoQbec_6VAekMPV1MjUP5k465g52D_3594.600s_3624.600s_audio.mov
MATeR
0L0j6X6cf3DO1Bs0D0K4Ch_0HfelmyAjA2a6Z6r7UQ6pe_116s_145.900s.mov
HiBERT
0L0j6X6cf3DO1Bs0D0K4Ch_0HfelmyAjA2a6Z6r7UQ6pe_266s_295.800s_audio.mov
MATeR-textonly
0L0j6X6cf3DO1Bs0D0K4Ch_0HfelmyAjA2a6Z6r7UQ6pe_30.400s_50.500s_audio.mov
Audio samples for cases where MATeR generated summaries obtain significant lower ROUGE-2 F1 score if compared with the best competitor.
MATeR
7lODos6uX9G0hRGtaBFgr6_5OmsBCCphxwDEpGudThv6s_215.800s_244.900s.mov
HiBERT
7lODos6uX9G0hRGtaBFgr6_5OmsBCCphxwDEpGudThv6s_72.700s_102.500s_audio.mov
MATeR-textonly
7lODos6uX9G0hRGtaBFgr6_5OmsBCCphxwDEpGudThv6s_3484s_3513.700s_audio.mov
MATeR
2KBfl8eidzorW02RzQf9K8_51nmU0wf4wR6wVHACEagPs_1647.900s_1676.900s.mov
HiBERT
2KBfl8eidzorW02RzQf9K8_51nmU0wf4wR6wVHACEagPs_13.300s_42.400s_audio.mov
MATeR-textonly
2KBfl8eidzorW02RzQf9K8_51nmU0wf4wR6wVHACEagPs_1010.500s_1040.300s_audio.mov
MATeR
51Bg4WCSE54ldyM7K4Nzff_3GSeNWxX70abttVM99zWsy_109.400s_139.200s.mov
HiBERT
51Bg4WCSE54ldyM7K4Nzff_3GSeNWxX70abttVM99zWsy_13.400s_41.700s_audio.mov
MATeR-textonly