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

arXiv 2403.02930 ↗First posted 5 Mar 2024cs.CL cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 24 of its 35 references that are in the map. First posted on arXiv 5 Mar 2024.

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References · 24

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12 more
  1. 2017A Deep Reinforced Model for Abstractive Summarizationcited by 365
  2. 2021Do Transformers Really Perform Bad for Graph Representation?cited by 320
  3. 2019BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarizationcited by 156
  4. 2020GSum: A General Framework for Guided Neural Abstractive Summarizationcited by 129
  5. 2019Discourse-Aware Neural Extractive Text Summarizationcited by 105
  6. 2020Knowledge Graph-Augmented Abstractive Summarization with Semantic-Driven Cloze Rewardcited by 66
  7. 2018Simpler but More Accurate Semantic Dependency Parsingcited by 46
  8. 2019Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document Inputscited by 38
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  11. 2021BASS: Boosting Abstractive Summarization with Unified Semantic Graphcited by 21
  12. 2021Word Graph Guided Summarization for Radiology Findingscited by 19

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