Papers by Pinelopi Papalampidi
Movie Plot Analysis via Turning Point Identification (D19-1)
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| Challenge: | Using computational literary analysis, we analyze novels, plays, and screenplays for their turning points. |
| Approach: | They propose to use turning points to analyze screenplays and plot synopses as tools for analysis . they propose to build a neural network model that identifies turning points in plot synoopse . |
| Outcome: | The proposed model outperforms baselines based on state-of-the-art sentence representations and expected position of turning points. |
Do LLMs Really Need 10+ Thoughts for “Find the Time 1000 Days Later”? Towards Structural Understanding of LLM Overthinking (2026.acl-long)
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Xinliang Frederick Zhang, Anhad Mohananey, Alexandra Chronopoulou, Pinelopi Papalampidi, Somit Gupta, Tsendsuren Munkhdalai, Lu Wang, Shyam Upadhyay
| Challenge: | Existing studies on LLMs' thought processes are limited to superficial, profiling-based observations, failing to delve into their inner workings. |
| Approach: | They propose a utility-based definition of overthinking that moves beyond length-based metrics and provides a more insightful understanding of LLMs' thought progression. |
| Outcome: | The proposed model decomposes the LLM thought process into minimally complete sub-thoughts and identifies common thinking patterns for topically similar queries. |
Screenplay Summarization Using Latent Narrative Structure (2020.acl-main)
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| Challenge: | Experimental results show that latent turning points improve summarization performance over general extractive summarizing models. |
| Approach: | They propose to explicitly incorporate the underlying structure of narratives into extractive summarization models by treating it as latent. |
| Outcome: | The proposed model improves on the CSI corpus of screenplays on a CSI episode . it shows that latent turning points correlate with important aspects of the document . |
Hierarchical3D Adapters for Long Video-to-text Summarization (2023.findings-eacl)
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| Challenge: | a recent study shows that multimodal summarization is not efficient for long inputs and outputs. |
| Approach: | They extend a TV episode transcript summarization dataset and create a multimodal variant by collecting full-length videos. |
| Outcome: | The proposed model can be tuned to perform multimodal summarization tasks efficiently using adapter modules augmented with a hierarchical structure while tuning only 3.8% of model parameters. |