7 research outputs found
A framework for bibliographic recommendation system based on Heterogeneous Retrieval Model?
In this paper, we propose an architectural framework for recommending heterogeneous resources in a digital library.We present an outline of our proposed recommendation framework, and discuss brie its performance over SpringerNature SciGraph¹ dataset
Segmenting Scientific Abstracts into Discourse Categories: A Deep Learning-Based Approach for Sparse Labeled Data
The abstract of a scientific paper distills the contents of the paper into a
short paragraph. In the biomedical literature, it is customary to structure an
abstract into discourse categories like BACKGROUND, OBJECTIVE, METHOD, RESULT,
and CONCLUSION, but this segmentation is uncommon in other fields like computer
science. Explicit categories could be helpful for more granular, that is,
discourse-level search and recommendation. The sparsity of labeled data makes
it challenging to construct supervised machine learning solutions for automatic
discourse-level segmentation of abstracts in non-bio domains. In this paper, we
address this problem using transfer learning. In particular, we define three
discourse categories BACKGROUND, TECHNIQUE, OBSERVATION-for an abstract because
these three categories are the most common. We train a deep neural network on
structured abstracts from PubMed, then fine-tune it on a small hand-labeled
corpus of computer science papers. We observe an accuracy of 75% on the test
corpus. We perform an ablation study to highlight the roles of the different
parts of the model. Our method appears to be a promising solution to the
automatic segmentation of abstracts, where the labeled data is sparse.Comment: to appear in the proceedings of JCDL'202
Generation of Highlights from Research Papers Using Pointer-Generator Networks and SciBERT Embeddings
Nowadays many research articles are prefaced with research highlights to
summarize the main findings of the paper. Highlights not only help researchers
precisely and quickly identify the contributions of a paper, they also enhance
the discoverability of the article via search engines. We aim to automatically
construct research highlights given certain segments of the research paper. We
use a pointer-generator network with coverage mechanism and a contextual
embedding layer at the input that encodes the input tokens into SciBERT
embeddings. We test our model on a benchmark dataset, CSPubSum and also present
MixSub, a new multi-disciplinary corpus of papers for automatic research
highlight generation. For both CSPubSum and MixSub, we have observed that the
proposed model achieves the best performance compared to related variants and
other models proposed in the literature. On the CSPubSum data set, our model
achieves the best performance when the input is only the abstract of a paper as
opposed to other segments of the paper. It produces ROUGE-1, ROUGE-2 and
ROUGE-L F1-scores of 38.26, 14.26 and 35.51, respectively, METEOR F1-score of
32.62, and BERTScore F1 of 86.65 which outperform all other baselines. On the
new MixSub data set, where only the abstract is the input, our proposed model
(when trained on the whole training corpus without distinguishing between the
subject categories) achieves ROUGE-1, ROUGE-2 and ROUGE-L F1-scores of 31.78,
9.76 and 29.3, respectively, METEOR F1-score of 24.00, and BERTScore F1 of
85.25, outperforming other models.Comment: 18 pages, 7 figures, 7 table