Authors: Hiroaki Hayashi, Wojciech Kryściński, Bryan McCann, Nazneen Rajani, Caiming Xiong

Paper

With thousands of academic articles shared on a daily basis, it has become increasingly difficult to keep up with the latest scientific findings. To overcome this problem, we introduce a new task of disentangled paper summarization, which seeks to generate separate summaries for the paper contributions and the context of the work, making it easier to identify the key findings shared in articles. For this purpose, we extend the S2ORC corpus of academic articles, which spans a diverse set of domains ranging from economics to psychology, by adding disentangled contribution and context reference labels. Together with the dataset, we introduce and analyze three baseline approaches:

1. a unified model controlled by input code prefixes,
2. a model with separate generation heads specialized in generating the disentangled outputs, and
3. a training strategy that guides the model using additional supervision coming from inbound and outbound citations.

We also propose a comprehensive automatic evaluation protocol which reports the relevance, novelty, and disentanglement of generated outputs. Through a human study involving expert annotators, we show that in 79%, of cases our new task is considered more helpful than traditional scientific paper summarization.

1. Updates
2. Usage
3. Citation
4. License

11/9/20: Initial code release.

First, install the requirements with requirements.txt. Please note that we specify transformers==2.11.0.

Follow the procedure here.

Use the corresponding scripts in scripts/ directory to train and decode from the models after modifying the --datadir in the scripts. For example, training ControlCode model would be

./scripts/train_controlcode.sh exp1

where exp1 is the name of experiment to store all the related data and checkpoints.

Use the corresponding scripts starting with eval_. For example, below is the command for evaluating the model trained with the above command.

./scripts/eval_controlcode.sh exp1 TARGET_TYPE RESULT_DIR SPLIT

Follow the instruction here for setting up the evaluation tool.

For our three-way evaluation, each instance require four summaries as shown on the right: gold standard (contribution, context) summaries and generated (contribution, context) summaries. ROUGE scores between paired summaries in the figure is calculated first and are aggregated to represent relevance, purity, and disentanglement.

After decoding texts from different models in different modes, you can run src/evaluate.py to get ROUGE and BERTScore results. Usage is as follows:

# Example 1: Comparing references and decoded texts within exp1.
python evaluate.py \
    --exps /path/to/exp1 \
    --output-file exp1-ref_exp1-dec.jsonl

# Example 2: Comparing references from exp1 and decoded texts from exp2.
python evaluate.py \
    --exps /path/to/exp1 /path/to/exp2  \
    --summary-type reference decoded \
    --output-file exp1-ref_exp2-dec.jsonl

Using the scripts above, you can obtain all the pairwise evaluations associated to each line on the right figure. Finally, use src/calc_metrics.py to compute the measures used for this task:

# Relevance:
python calc_metrics.py \
    --contrib-rel /path/to/s_con_y_con.jsonl \
    --context-rel /path/to/s_ctx_y_ctx.jsonl \
    --measure relevance

# Purity:
python calc_metrics.py \
    --contrib-rel /path/to/s_con_y_con.jsonl \
    --context-rel /path/to/s_ctx_y_ctx.jsonl \
    --contrib-cross /path/to/s_con_y_ctx.jsonl \
    --context-cross /path/to/s_ctx_y_con.jsonl \
    --measure purity

# Disentanglement:
python calc_metrics.py \
    --contrib-context /path/to/s_con_s_ctx.jsonl \
    --measure disentanglement

See the file for the argument details.

@article{hayashi2020whats,
  author    = {Hiroaki Hayashi and Wojciech Kry{\'s}ci{\'n}ski and Bryan McCann and Nazneen Rajani and Caiming Xiong},
  title     = {What's new? Summarizing Contributions in Scientific Literature},
  journal   = {arXiv preprint arXiv:2011.03161},
  year      = {2020},
}

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