A Second Look on BASS -- Boosting Abstractive Summarization with Unified Semantic Graphs -- A Replication Study

Fuente: arXiv
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Autori principali: Koraş, Osman Alperen, Schlötterer, Jörg, Seifert, Christin
Natura: Preprint
Pubblicazione: 2024
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author Koraş, Osman Alperen
Schlötterer, Jörg
Seifert, Christin
author_facet Koraş, Osman Alperen
Schlötterer, Jörg
Seifert, Christin
contents We present a detailed replication study of the BASS framework, an abstractive summarization system based on the notion of Unified Semantic Graphs. Our investigation includes challenges in replicating key components and an ablation study to systematically isolate error sources rooted in replicating novel components. Our findings reveal discrepancies in performance compared to the original work. We highlight the significance of paying careful attention even to reasonably omitted details for replicating advanced frameworks like BASS, and emphasize key practices for writing replicable papers.
format Preprint
id arxiv_https___arxiv_org_abs_2403_02930
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle A Second Look on BASS -- Boosting Abstractive Summarization with Unified Semantic Graphs -- A Replication Study
Koraş, Osman Alperen
Schlötterer, Jörg
Seifert, Christin
Computation and Language
Machine Learning
We present a detailed replication study of the BASS framework, an abstractive summarization system based on the notion of Unified Semantic Graphs. Our investigation includes challenges in replicating key components and an ablation study to systematically isolate error sources rooted in replicating novel components. Our findings reveal discrepancies in performance compared to the original work. We highlight the significance of paying careful attention even to reasonably omitted details for replicating advanced frameworks like BASS, and emphasize key practices for writing replicable papers.
title A Second Look on BASS -- Boosting Abstractive Summarization with Unified Semantic Graphs -- A Replication Study
topic Computation and Language
Machine Learning
url https://arxiv.org/abs/2403.02930