| Challenge: | Podcast episodes often contain extraneous material interleaved within the audio and the written descriptions . authors present classifiers that leverage both textual and listening patterns to detect such content . |
| Approach: | They propose a classifier that leverages both textual and listening patterns to detect extraneous material in podcast descriptions and audio transcripts. |
| Outcome: | The proposed classifiers improve ROUGE scores and reduce extraneous content in podcast summarization tasks. |
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| Challenge: | Existing methods to study narrativity in novels, social media and patient records are limited. |
| Approach: | They propose to process podcast transcripts and extract narrative content from podcasts . they use annotations to enable future research into narrativity within a large corpus of podcast episodes. |
| Outcome: | The proposed methods compare to existing methods and can enable future research into narrativity within a large corpus of approximately 100,000 podcast episodes. |
Towards Abstractive Grounded Summarization of Podcast Transcripts (2022.acl-long)
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| Challenge: | Podcast summarization is of practical benefit to content providers and consumers . however, podcast summarizing faces significant challenges including factual inconsistencies . speech recognizers induce transcription errors and abstractive summarisation models may hallucinate . |
| Approach: | They propose a method to generate podcast summaries while grounding segments in specific regions of the transcript to allow full inspection of summary details. |
| Outcome: | The proposed method can produce an abstractive summary while grounding segments in specific regions of the transcript to allow full inspection of summary details. |
Mapping the Podcast Ecosystem with the Structured Podcast Research Corpus (2025.acl-long)
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| Challenge: | a dataset of over 1.1M podcast transcripts is largely comprehensive of all English language podcasts available through public RSS feeds from May and June of 2020. |
| Approach: | They propose to build a large-scale open dataset of podcast transcripts that includes metadata, speaker roles, audio features and speaker turns for a subset of 370K episodes. |
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Modeling Language Usage and Listener Engagement in Podcasts (2021.acl-long)
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| Challenge: | linguistic factors such as vocabulary diversity, distinctiveness, emotion, and syntax are highly predictive of engagement in podcasts, but little research has been done into how they contribute to overall listener engagement. |
| Approach: | They build models with different textual representations to test popular wisdom about stylistic elements in high-engagement podcasts, corroborating some pieces of advice and adding new perspectives on others. |
| Outcome: | The proposed models show that stylistic features are highly predictive of engagement for podcasts with low absolute streams than for the most popular podcasts. |
Annotating and Modeling Fine-grained Factuality in Summarization (2021.naacl-main)
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| Challenge: | Recent abstractive summarization systems produce factual errors that are not faithful to the input . current methods are lacking in identifying what errors are most important to target . |
| Approach: | They use synthetic and human-labeled data to identify factual errors in summarization and train models on the factuality detection task. |
| Outcome: | The proposed model detects factual errors on word, dependency, and sentence levels. |
NonFactS: NonFactual Summary Generation for Factuality Evaluation in Document Summarization (2023.findings-acl)
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| Challenge: | Pre-trained abstractive summarization models generate fluent summaries that are inconsistent with context document and contain nonfactual information. |
| Approach: | They propose a data generation model that synthesizes nonfactual summaries using human annotations. |
| Outcome: | The proposed model can generate nonfactual summaries and generalize to out-of-domain documents. |
100,000 Podcasts: A Spoken English Document Corpus (2020.coling-main)
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Ann Clifton, Sravana Reddy, Yongze Yu, Aasish Pappu, Rezvaneh Rezapour, Hamed Bonab, Maria Eskevich, Gareth Jones, Jussi Karlgren, Ben Carterette, Rosie Jones
| Challenge: | Podcasts are a large and growing repository of spoken audio. |
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Discourse-Driven Evaluation: Unveiling Factual Inconsistency in Long Document Summarization (2025.naacl-long)
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| Challenge: | Existing summarization systems can generate fluent summaries, but their ability to produce factually consistent summary remains questionable. |
| Approach: | They propose a framework that decomposes long texts into discourse-inspired chunks and utilizes discourse information to better aggregate sentence-level scores predicted by NLI models. |
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Annotating and Detecting Fine-grained Factual Errors for Dialogue Summarization (2023.acl-long)
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| Challenge: | Existing work on factual inconsistency in abstractive summarization addresses this problem. |
| Approach: | They propose a dataset with fine-grained factual error annotations named DIASUMFACT and an unsupervised model named ENDERANKER. |
| Outcome: | The proposed model performs on par with the state-of-the-art models while requiring fewer resources. |
What you can cram into a single $&!#* vector: Probing sentence embeddings for linguistic properties (P18-1)
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| Challenge: | a lack of understanding of the properties of sentence embeddings is limiting the use of the techniques. |
| Approach: | They propose 10 probing tasks designed to capture simple linguistic features of sentences . they use three different encoders to train embeddings in eight different ways . |
| Outcome: | The proposed tasks capture key linguistic features of sentences, but they are difficult to infer from them. |