title	Researcher
emoji	🤓
colorFrom	gray
colorTo	pink
sdk	streamlit
sdk_version	1.2.0
app_file	app.py
pinned	false

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

This work builds upon these fantastic models (for various nlp sub-tasks) out there for researchers and devs like us https://huggingface.co/Callidior/bert2bert-base-arxiv-titlegenhttps://huggingface.co/allenai/scibert_scivocab_uncasedhttps://huggingface.co/allenai/led-large-16384-arxivhttps://huggingface.co/sentence-transformers/paraphrase-MiniLM-L6-v2https://huggingface.co/sentence-transformers/distilbert-base-nli-mean-tokenshttps://tabula.technology/https://spacy.io/ and https://allenai.github.io/scispacy/

Please cite this repo if it helped you :)