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README.md

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+license: cc-by-4.0
+language:
+- en
+pretty_name: Talk2Ref
+size_categories:
+- 1K<n<10K
 ---
+
+# Talk2Ref: A Dataset for Reference Prediction from Scientific Talks
+
+Scientific talks are a growing medium for disseminating research, and automatically identifying relevant literature that
+grounds or enriches a talk would be highly valuable for researchers and students alike. We introduce **Reference
+Prediction from Talks (RPT)**, a new task that maps long and unstructured scientific presentations to relevant papers.  
+To support research on RPT, we present **Talk2Ref**, the first large-scale dataset of its kind, containing **6,279 talks**
+and **43,429 cited papers** (26 per talk on average), where relevance is approximated by the papers cited in the talk’s
+corresponding source publication.  
+
+We establish strong baselines by evaluating state-of-the-art text embedding
+models in zero-shot retrieval scenarios and propose a **dual-encoder architecture** trained on Talk2Ref. We further
+explore strategies for handling long transcripts and training for domain adaptation.  
+Our results show that fine-tuning on Talk2Ref significantly improves citation prediction performance, demonstrating both
+the challenges of the task and the effectiveness of our dataset for learning semantic representations from spoken scientific content.
+
+The dataset and trained models are released under an open license to foster future research on integrating spoken
+scientific communication into citation recommendation systems.
+
+---
+
+## Dataset Summary
+
+To the best of our knowledge, **no existing dataset** supports research on Reference Prediction from Talks (RPT).
+**Talk2Ref** is the first large-scale resource pairing scientific presentations with their corresponding relevant papers.
+Relevance is modeled using the citations in each talk’s source publication.  
+
+Talk2Ref includes:
+- **6,279 scientific talks**
+- **43,429 cited papers**
+- **≈26 references per talk**
+- Spanning **2017–2022**
+- Covering **ACL, NAACL, and EMNLP conferences**
+
+This dataset provides a foundation for systematically studying reference prediction from spoken scientific content at scale.
+
+---
+
+## Dataset Structure
+
+| Split | Conferences | Years | Talks | Avg. Length (min) | Avg. Words | Avg. References | Total References |
+|:------|:-------------|:------|------:|------------------:|------------:|----------------:|-----------------:|
+| Train | ACL, NAACL, EMNLP | 2017–2021 | 3,971 | 12.1 | 1615 | 26.75 | 31,064 |
+| Dev | ACL | 2022 | 882 | 9.9 | 1327 | 26.05 | 11,805 |
+| Test | EMNLP, NAACL | 2022 | 1,426 | 9.1 | 1186 | 25.66 | 16,935 |
+| **Total** | ACL, NAACL, EMNLP | **2017–2022** | **6,279** | **11.1** | **1,478** | **26.4** | **43,429** |
+
+Talks are partitioned chronologically by conference year.  
+Earlier years form the training split (2017–2021), and later years (2022) are used for development and testing,
+ensuring **temporal consistency** between splits.
+
+---
+
+## Dataset Fields
+
+| Field | Type | Description |
+|:------|:-----|:-------------|
+| `video_path` | string | URL or path to the original conference talk video. |
+| `audio` | audio | Audio waveform of the talk segment with sampling rate information. |
+| `sr` | int | Sampling rate (Hz) of the audio recording. |
+| `abstract` | string | Abstract of the corresponding scientific paper. |
+| `language` | string | Language of the talk (English). |
+| `split` | string | Split name (“train”, “dev”, or “test”). |
+| `duration` | float | Duration of the audio in seconds. |
+| `conference` | string | Conference name (ACL, NAACL, or EMNLP). |
+| `year` | string | Year of the conference. |
+| `transcription` | string | Automatic speech recognition (ASR) transcript of the talk. |
+| `title` | string | Paper title associated with the talk. |
+| `references` | list | List of structured metadata for cited papers, including title, authors, abstract, year. |
+
+---
+
+## Data Collection and Processing
+
+1. **Source Acquisition:**  
+   Conference talks and associated papers were obtained from the **ACL Anthology**.
+
+2. **Audio Extraction:**  
+   Audio tracks were extracted from videos and converted to `.wav` format using FFmpeg.
+
+3. **Transcription:**  
+   Speech was transcribed using **Whisper-Large-v3**.
+
+4. **Reference Extraction:**  
+   The corresponding paper PDFs were parsed with **GROBID**, extracting all cited references and metadata.
+
+5. **Abstract Retrieval:**  
+   Missing abstracts were filled by querying **CrossRef**, **arXiv**, **OpenAlex**, and **Semantic Scholar**.
+
+6. **Filtering:**  
+   Invalid or placeholder abstracts were removed.
+
+This process results in a rich dataset linking each talk to its cited papers, including audio, transcript, and metadata.
+
+---
+
+## Use Cases
+
+Talk2Ref supports research on:
+- **Reference Prediction from Spoken Content**
+- **Speech-to-Text and Speech-to-Abstract Generation**
+- **Retrieval and Representation Learning**
+
+---
+
+## Licensing
+
+The dataset is distributed under the **Creative Commons Attribution 4.0 International License (CC BY 4.0)**.  
+Users are free to share and adapt the dataset with appropriate attribution.
+
+---