https://github.com/liyucheng09/hiporank

https://github.com/liyucheng09/hiporank

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• Host: GitHub
• URL: https://github.com/liyucheng09/hiporank
• Owner: liyucheng09
• Created: 2022-03-29T14:39:27.000Z (almost 3 years ago)
• Default Branch: master
• Last Pushed: 2022-03-29T17:54:12.000Z (almost 3 years ago)
• Last Synced: 2024-10-30T03:50:28.463Z (2 months ago)
• Language: Jupyter Notebook
• Size: 2.59 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# HipoRank

Unsupervised and extractive long document summarization with **Hi**erarchichal and **Po**sitional information. Contains code for [Discourse-Aware Unsupervised Summarization of Long Scientific Documents](https://arxiv.org/abs/2005.00513) accepted at EACL 2021.

# [更新]即插即用模型`inference.py`

使用最优的hyperparameters: `sentence-transformers`的`bert-base-nli`.

添加了从`sciencedirect.com`直接爬取paper的脚本。

直接输出至`data/inference/resluts`为`md`文件。

# Requirements

## Libraries

`pip install -r requirements.txt`

## Datasets

https://github.com/armancohan/long-summarization

## ROUGE

`pyrouge_set_rouge_path /absolute/path/to/ROUGE-1.5.5/directory`

## Model Files

- Biomed w2v: http://evexdb.org/pmresources/vec-space-models/wikipedia-pubmed-and-PMC-w2v.bin

- PacSum BERT: https://drive.google.com/file/d/1wbMlLmnbD_0j7Qs8YY8cSCh935WKKdsP/view?usp=sharing

- BioBERT: https://github.com/dmis-lab/biobert

- sentence-transformers: https://github.com/UKPLab/sentence-transformers 

# Directory Structure

## hipo_rank

Library to define summarization pipeline as modular components with standard interfaces for ease of experimentation;

- `hipo_rank/dataset_iterators` yield document sections and sentences in standard typed format;

- `hipo_rank/embedders` generate vector representations for sentences and sections;

- `hipo_rank/similarities` compute similarities to weigh edges in document graph;

- `hipo_rank/directions` introduce directionality in document graph based on discourse structure;

- `hipo_rank/scorers` compute node centrality for sentences in document graph;

- `hipo_rank/summarizers` generate a summary from a document graph with node centrality scores;

- `hipo_rank/evaluators` evaluate summary with different metrics;

## Experiments

- `exp1_run.py` hyperparameter tuning and ablation study on PubMed val set;

- `exp2_run.py` cnndm validation set;

- `exp3_run.py` pubmed test set;

- `exp4_run.py` arxiv test set with pubmed hyperparams;

- `exp5_run.py` lead baseline;

- `exp6_run.py` effect of document length;

- `exp7_run.py` hyperparameter tuning on arxiv val set;

- `exp8_run.py` pacsum baseline;

- `exp9_run.py` similarity thresholding;

- `exp10_run.py` short sentence removal;

- `exp11_run.py` TextRank baseline;

## Human evaluation

- `human_eval_sample.ipynb` Code to sample examples for human evaluation;

- `human_eval_samples.jsonl` Sampled examples for human evaluation;

- `human_eval_data.jsonl` Results of human evaluation from Prodigy.ai annotation tool;

- `human_eval_results.ipynb` Code to generate human evaluation metrics;

## Plotting

- `plot_ablation.ipynb` Code to plot results of ablation study;

- `plot_sentence_positions.ipynb` Code to plot sentence positions in original document;