title: Surveyor emoji: 📊 colorFrom: gray colorTo: pink sdk: streamlit sdk_version: 1.2.0 app_file: app.py pinned: false license: apache-2.0

Auto-Research

A no-code utility to generate a detailed well-cited survey with topic clustered sections (draft paper format) and other interesting artifacts from a single research query.

Data Provider: arXiv Open Archive Initiative OAI

Requirements:

python 3.7 or above
poppler-utils - sudo apt-get install build-essential libpoppler-cpp-dev pkg-config python-dev
list of requirements in requirements.txt - cat requirements.txt | xargs pip install
8GB disk space
13GB CUDA(GPU) memory - for a survey of 100 searched papers(max_search) and 25 selected papers(num_papers)

Demo :

Video Demo : https://drive.google.com/file/d/1-77J2L10lsW-bFDOGdTaPzSr_utY743g/view?usp=sharing

Kaggle Re-usable Demo : https://www.kaggle.com/sidharthpal/auto-research-generate-survey-from-query

([TIP] click 'edit and run' to run the demo for your custom queries on a free GPU)

Installation:

sudo apt-get install build-essential poppler-utils libpoppler-cpp-dev pkg-config python-dev
pip install git+https://github.com/sidphbot/Auto-Research.git

Run Survey (cli):

python survey.py [options] <your_research_query>

Run Survey (Streamlit web-interface - new):

streamlit run app.py

Run Survey (Python API):

from survey import Surveyor
mysurveyor = Surveyor()
mysurveyor.survey('quantum entanglement')

Research tools:

These are independent tools for your research or document text handling needs.

*[Tip]* :(models can be changed in defaults or passed on during init along with `refresh-models=True`)

abstractive_summary - takes a long text document (string) and returns a 1-paragraph abstract or “abstractive” summary (string)

Input:
```
  `longtext` : string
  
```
Returns:
```
  `summary` : string
```
extractive_summary - takes a long text document (string) and returns a 1-paragraph of extracted highlights or “extractive” summary (string)

Input:
```
  `longtext` : string
  
```
Returns:
```
  `summary` : string
```
generate_title - takes a long text document (string) and returns a generated title (string)

Input:
```
  `longtext` : string
  
```
Returns:
```
  `title` : string
```
extractive_highlights - takes a long text document (string) and returns a list of extracted highlights ([string]), a list of keywords ([string]) and key phrases ([string])

Input:
```
  `longtext` : string
  
```
Returns:
```
  `highlights` : [string]
  `keywords` : [string]
  `keyphrases` : [string]
```
extract_images_from_file - takes a pdf file name (string) and returns a list of image filenames ([string]).

Input:
```
  `pdf_file` : string
  
```
Returns:
```
  `images_files` : [string]
```
extract_tables_from_file - takes a pdf file name (string) and returns a list of csv filenames ([string]).

Input:
```
  `pdf_file` : string
  
```
Returns:
```
  `images_files` : [string]
```
cluster_lines - takes a list of lines (string) and returns the topic-clustered sections (dict(generated_title: [cluster_abstract])) and clustered lines (dict(cluster_id: [cluster_lines]))

Input:
```
  `lines` : [string]
  
```
Returns:
```
  `sections` : dict(generated_title: [cluster_abstract])
  `clusters` : dict(cluster_id: [cluster_lines])
```
extract_headings - [for scientific texts - Assumes an ‘abstract’ heading present] takes a text file name (string) and returns a list of headings ([string]) and refined lines ([string]).

[Tip 1] : Use extract_sections as a wrapper (e.g. extract_sections(extract_headings(“/path/to/textfile”)) to get heading-wise sectioned text with refined lines instead (dict( heading: text))

[Tip 2] : write the word ‘abstract’ at the start of the file text to get an extraction for non-scientific texts as well !!

Input:
```
  `text_file` : string 		
  
```
Returns:
```
  `refined` : [string], 
  `headings` : [string]
  `sectioned_doc` : dict( heading: text) (Optional - Wrapper case)
```

Access/Modify defaults:

inside code

from survey.Surveyor import DEFAULTS
from pprint import pprint

pprint(DEFAULTS)

or,

Modify static config file - defaults.py

or,

At runtime (utility)

python survey.py --help

usage: survey.py [-h] [--max_search max_metadata_papers]
                   [--num_papers max_num_papers] [--pdf_dir pdf_dir]
                   [--txt_dir txt_dir] [--img_dir img_dir] [--tab_dir tab_dir]
                   [--dump_dir dump_dir] [--models_dir save_models_dir]
                   [--title_model_name title_model_name]
                   [--ex_summ_model_name extractive_summ_model_name]
                   [--ledmodel_name ledmodel_name]
                   [--embedder_name sentence_embedder_name]
                   [--nlp_name spacy_model_name]
                   [--similarity_nlp_name similarity_nlp_name]
                   [--kw_model_name kw_model_name]
                   [--refresh_models refresh_models] [--high_gpu high_gpu]
                   query_string

Generate a survey just from a query !!

positional arguments:
  query_string          your research query/keywords

optional arguments:
  -h, --help            show this help message and exit
  --max_search max_metadata_papers
                        maximium number of papers to gaze at - defaults to 100
  --num_papers max_num_papers
                        maximium number of papers to download and analyse -
                        defaults to 25
  --pdf_dir pdf_dir     pdf paper storage directory - defaults to
                        arxiv_data/tarpdfs/
  --txt_dir txt_dir     text-converted paper storage directory - defaults to
                        arxiv_data/fulltext/
  --img_dir img_dir     image storage directory - defaults to
                        arxiv_data/images/
  --tab_dir tab_dir     tables storage directory - defaults to
                        arxiv_data/tables/
  --dump_dir dump_dir   all_output_dir - defaults to arxiv_dumps/
  --models_dir save_models_dir
                        directory to save models (> 5GB) - defaults to
                        saved_models/
  --title_model_name title_model_name
                        title model name/tag in hugging-face, defaults to
                        'Callidior/bert2bert-base-arxiv-titlegen'
  --ex_summ_model_name extractive_summ_model_name
                        extractive summary model name/tag in hugging-face,
                        defaults to 'allenai/scibert_scivocab_uncased'
  --ledmodel_name ledmodel_name
                        led model(for abstractive summary) name/tag in
                        hugging-face, defaults to 'allenai/led-
                        large-16384-arxiv'
  --embedder_name sentence_embedder_name
                        sentence embedder name/tag in hugging-face, defaults
                        to 'paraphrase-MiniLM-L6-v2'
  --nlp_name spacy_model_name
                        spacy model name/tag in hugging-face (if changed -
                        needs to be spacy-installed prior), defaults to
                        'en_core_sci_scibert'
  --similarity_nlp_name similarity_nlp_name
                        spacy downstream model(for similarity) name/tag in
                        hugging-face (if changed - needs to be spacy-installed
                        prior), defaults to 'en_core_sci_lg'
  --kw_model_name kw_model_name
                        keyword extraction model name/tag in hugging-face,
                        defaults to 'distilbert-base-nli-mean-tokens'
  --refresh_models refresh_models
                        Refresh model downloads with given names (needs
                        atleast one model name param above), defaults to False
  --high_gpu high_gpu   High GPU usage permitted, defaults to False

At runtime (code)

during surveyor object initialization with surveyor_obj = Surveyor()
- pdf_dir: String, pdf paper storage directory - defaults to arxiv_data/tarpdfs/
- txt_dir: String, text-converted paper storage directory - defaults to arxiv_data/fulltext/
- img_dir: String, image image storage directory - defaults to arxiv_data/images/
- tab_dir: String, tables storage directory - defaults to arxiv_data/tables/
- dump_dir: String, all_output_dir - defaults to arxiv_dumps/
- models_dir: String, directory to save to huge models, defaults to saved_models/
- title_model_name: String, title model name/tag in hugging-face, defaults to Callidior/bert2bert-base-arxiv-titlegen
- ex_summ_model_name: String, extractive summary model name/tag in hugging-face, defaults to allenai/scibert_scivocab_uncased
- ledmodel_name: String, led model(for abstractive summary) name/tag in hugging-face, defaults to allenai/led-large-16384-arxiv
- embedder_name: String, sentence embedder name/tag in hugging-face, defaults to paraphrase-MiniLM-L6-v2
- nlp_name: String, spacy model name/tag in hugging-face (if changed - needs to be spacy-installed prior), defaults to en_core_sci_scibert
- similarity_nlp_name: String, spacy downstream trained model(for similarity) name/tag in hugging-face (if changed - needs to be spacy-installed prior), defaults to en_core_sci_lg
- kw_model_name: String, keyword extraction model name/tag in hugging-face, defaults to distilbert-base-nli-mean-tokens
- high_gpu: Bool, High GPU usage permitted, defaults to False
- refresh_models: Bool, Refresh model downloads with given names (needs atleast one model name param above), defaults to False
during survey generation with surveyor_obj.survey(query="my_research_query")
- max_search: int maximium number of papers to gaze at - defaults to 100
- num_papers: int maximium number of papers to download and analyse - defaults to 25

Artifacts generated (zipped):

Detailed survey draft paper as txt file
A curated list of top 25+ papers as pdfs and txts
Images extracted from above papers as jpegs, bmps etc
Heading/Section wise highlights extracted from above papers as a re-usable pure python joblib dump
Tables extracted from papers(optional)
Corpus of metadata highlights/text of top 100 papers as a re-usable pure python joblib dump

Please cite this repo if it helped you :)