Abstractive Text Summarization based on Language Model Conditioning and Locality Modeling
Dmitrii Aksenov, Julian Moreno-Schneider, Peter Bourgonje, Robert Schwarzenberg, Leonhard Hennig, Georg Rehm
Abstract
We explore to what extent knowledge about the pre-trained language model that is used is beneficial for the task of abstractive summarization. To this end, we experiment with conditioning the encoder and decoder of a Transformer-based neural model on the BERT language model. In addition, we propose a new method of BERT-windowing, which allows chunk-wise processing of texts longer than the BERT window size. We also explore how locality modeling, i.e., the explicit restriction of calculations to the local context, can affect the summarization ability of the Transformer. This is done by introducing 2-dimensional convolutional self-attention into the first layers of the encoder. The results of our models are compared to a baseline and the state-of-the-art models on the CNN/Daily Mail dataset. We additionally train our model on the SwissText dataset to demonstrate usability on German. Both models outperform the baseline in ROUGE scores on two datasets and show its superiority in a manual qualitative analysis.- Anthology ID:
- 2020.lrec-1.825
- Volume:
- Proceedings of the Twelfth Language Resources and Evaluation Conference
- Month:
- May
- Year:
- 2020
- Address:
- Marseille, France
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 6680–6689
- Language:
- English
- URL:
- https://aclanthology.org/2020.lrec-1.825
- DOI:
- Cite (ACL):
- Dmitrii Aksenov, Julian Moreno-Schneider, Peter Bourgonje, Robert Schwarzenberg, Leonhard Hennig, and Georg Rehm. 2020. Abstractive Text Summarization based on Language Model Conditioning and Locality Modeling. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 6680–6689, Marseille, France. European Language Resources Association.
- Cite (Informal):
- Abstractive Text Summarization based on Language Model Conditioning and Locality Modeling (Aksenov et al., LREC 2020)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2020.lrec-1.825.pdf
- Code
- axenov/BERT-Summ-OpenNMT
- Data
- CNN/Daily Mail