Sentence-wise Speech Summarization: Task, Datasets, and End-to-End
  Modeling with LM Knowledge Distillation
This paper introduces a novel approach called sentence-wise speech
summarization (Sen-SSum), which generates text summaries from a spoken document
in a sentence-by-sentence manner. Sen-SSum combines the real-time processing of
automatic speech recognition (ASR) with the conciseness of speech
summarization. To explore this approach, we present two datasets for Sen-SSum:
Mega-SSum and CSJ-SSum. Using these datasets, our study evaluates two types of
Transformer-based models: 1) cascade models that combine ASR and strong text
summarization models, and 2) end-to-end (E2E) models that directly convert
speech into a text summary. While E2E models are appealing to develop
compute-efficient models, they perform worse than cascade models. Therefore, we
propose knowledge distillation for E2E models using pseudo-summaries generated
by the cascade models. Our experiments show that this proposed knowledge
distillation effectively improves the performance of the E2E model on both
datasets.
66ac3bdbae2981107ddb3890
Kohei Matsuura
admin_assigned
2024-08-02T09:24:25.010Z
645092abfc5e5ff919b4aa0a
/avatars/72e5d4db1e489924d03ab30041923a41.svg
Kohei Matsuura
user
komats
66ac3bdbae2981107ddb3891
Takanori Ashihara
admin_assigned
2024-08-02T09:24:30.725Z
631807e61d6018cb851c47bb
/avatars/5e7e92b4dda08ad385bd858c35677577.svg
Takanori Ashihara
user
ashi-ta
66ac3bdbae2981107ddb3892
Takafumi Moriya
admin_assigned
2024-08-02T09:24:36.514Z
664ef5d055bbfe79416102b2
/avatars/8eca89f2d5f24420b2f99d4be01c2d2c.svg
Takafumi Moriya
user
tmoriya777
66ac3bdbae2981107ddb3893
Masato Mimura
admin_assigned
2024-08-02T09:24:41.551Z
65d56a4187c889ddc40769c9
/avatars/3f0df20f3b98b980bfbe0219b0a3a63c.svg
Masato Mimura
user
mimura
66ac3bdbae2981107ddb3894
Takatomo Kano
admin_assigned
2024-08-02T09:24:46.869Z
64f19c83963e4bc2b7cc9dd7
/avatars/e0b9854dad00ca3cf604ead1401d3719.svg
takatomo kano
user
tkano
66ac3bdbae2981107ddb3895
Atsunori Ogawa
admin_assigned
2024-08-02T09:24:53.393Z
651d1582a4e688e310ce7c81
/avatars/8d75a4e4b97046f24b01f9158f8adea4.svg
Atsunori Ogawa
user
atsunori0108
66ac3bdbae2981107ddb3896
Marc Delcroix
admin_assigned
2024-08-02T09:24:58.750Z
6459a69c5a263a4c87b72d0e
/avatars/efff233120a8e727e90a74779d520cbb.svg
Marc Delcroix
user
mdelcroix
Sentence-wise Speech Summarization: Task, Datasets, and End-to-End
  Modeling with LM Knowledge Distillation
This paper introduces a novel approach called sentence-wise speech
summarization (Sen-SSum), which generates text summaries from a spoken document
in a sentence-by-sentence manner. Sen-SSum combines the real-time processing of
automatic speech recognition (ASR) with the conciseness of speech
summarization. To explore this approach, we present two datasets for Sen-SSum:
Mega-SSum and CSJ-SSum. Using these datasets, our study evaluates two types of
Transformer-based models: 1) cascade models that combine ASR and strong text
summarization models, and 2) end-to-end (E2E) models that directly convert
speech into a text summary. While E2E models are appealing to develop
compute-efficient models, they perform worse than cascade models. Therefore, we
propose knowledge distillation for E2E models using pseudo-summaries generated
by the cascade models. Our experiments show that this proposed knowledge
distillation effectively improves the performance of the E2E model on both
datasets.
A sentence-wise speech summarization approach combines ASR with text summarization using cascade and end-to-end Transformer models, with knowledge distillation improving E2E model performance.
automatic speech recognition speech summarization Transformer-based models cascade models end-to-end models knowledge distillation