47 citations · 143 across the 42 of their papers we have counts for
11 papers · 1 filter
Reusing Latent Speech Representations for Query-Conditioned Topic Localization in Transcripts
Steffen Freisinger, Philipp Seeberger, Thomas Ranzenberger +2
Long transcripts are costly inputs for downstream NLP systems and often contain irrelevant context. We study query-conditioned topic localization: predicting the sentence span in a…
Reading Between the Waves: Robust Topic Segmentation Using Inter-Sentence Audio Features
Steffen Freisinger, Philipp Seeberger, Tobias Bocklet +1
Spoken content, such as online videos and podcasts, often spans multiple topics, which makes automatic topic segmentation essential for user navigation and downstream applications.…
Towards Multi-Level Transcript Segmentation: LoRA Fine-Tuning for Table-of-Contents Generation
Steffen Freisinger, Philipp Seeberger, Thomas Ranzenberger +2
Segmenting speech transcripts into thematic sections benefits both downstream processing and users who depend on written text for accessibility. We introduce a novel approach to hi…
Generalizing to Unseen Disaster Events: A Causal View
Philipp Seeberger, Steffen Freisinger, Tobias Bocklet +1
Due to the rapid growth of social media platforms, these tools have become essential for monitoring information during ongoing disaster events. However, extracting valuable insight…
MMUTF: Multimodal Multimedia Event Argument Extraction with Unified Template Filling
Philipp Seeberger, Dominik Wagner, Korbinian Riedhammer
With the advancement of multimedia technologies, news documents and user-generated content are often represented as multiple modalities, making Multimedia Event Extraction (MEE) an…
Multi-Query Focused Disaster Summarization via Instruction-Based Prompting
Philipp Seeberger, Korbinian Riedhammer
Automatic summarization of mass-emergency events plays a critical role in disaster management. The second edition of CrisisFACTS aims to advance disaster summarization based on mul…
Information Type Classification with Contrastive Task-Specialized Sentence Encoders
Philipp Seeberger, Tobias Bocklet, Korbinian Riedhammer
User-generated information content has become an important information source in crisis situations. However, classification models suffer from noise and event-related biases which…
Combining Deep Neural Reranking and Unsupervised Extraction for Multi-Query Focused Summarization
Philipp Seeberger, Korbinian Riedhammer
The CrisisFACTS Track aims to tackle challenges such as multi-stream fact-finding in the domain of event tracking; participants' systems extract important facts from several disast…
Enhancing Crisis-Related Tweet Classification with Entity-Masked Language Modeling and Multi-Task Learning
Philipp Seeberger, Korbinian Riedhammer
Social media has become an important information source for crisis management and provides quick access to ongoing developments and critical information. However, classification mo…
Detecting Emotion Carriers by Combining Acoustic and Lexical Representations
Sebastian P. Bayerl, Aniruddha Tammewar, Korbinian Riedhammer +1
Personal narratives (PN) - spoken or written - are recollections of facts, people, events, and thoughts from one's own experience. Emotion recognition and sentiment analysis tasks…
A Comparison of Hybrid and End-to-End Models for Syllable Recognition
Sebastian P. Bayerl, Korbinian Riedhammer
This paper presents a comparison of a traditional hybrid speech recognition system (kaldi using WFST and TDNN with lattice-free MMI) and a lexicon-free end-to-end (TensorFlow imple…