TLDR: Extreme Summarization of Scientific Documents
arxiv.org
arxiv.org
I took a narrative detour I wanted to share:
Suppose we make the analogue of a scientific paper to a piece of mineral ore (in terms of their raw content, and without written symbols in them for the sake of the analogy) extracted from some mine or quarry. This ore is somehow useful to someone, even if its value is structural: the shingles on an academic roof or a heavyweight desk. What a summarizer attempts to do is use a generic refinement process that will grind up the ore and then separate the components of interest such as Iron, Uranium, or Gold.
Anyone thinking that all of metallurgy reduces to simply throwing the slab into a machine and have it spew out the precious metals will find, instead, more complexity than they bargained for, and have more questions on machines or methods to resolve. Gold, Iron, Uranium, all have different extraction process.
I believe this approach may give some insight in what problems to solve instead with AI: focus on those discoveries that have helped advance "metallurgy", those of discovering and understanding the structure of the mineral ore and contents (scientific papers) and their relation with current technologies at the time, not on the philosopher's stone of 'summarizing' process more akin to a hammer that makes everything seem like a nail.
Highly intelligent human beings have a natural ability to summarize big ideas into TLDRs. Are humans basically a bunch of "summarizers"? Probably not. Is this ability to summarize or compress big ideas into smaller, more condensed pieces of information, important to the human race? Yes, I would say that they are. So to me, this is certainly one of those problems that we correctly attempt to solve.
In short, here's the major differences:
> SciTLDR contains both author-written and expert-derived TLDRs
> CATTS improves upon strong baselines under both automated metrics and human evaluations
A clinician scanning a medical paper is looking for patient relevance: should they use the approach described? The statistical details are too intimidating, the preamble is irrelevant, they know the scope of the problem already.
This is not what "should" happen, but it is what actually happens.
The gap between published findings and clinical practice is several years. The peer review and publication process are way out of touch with clinical reality.
On top of this, people find articles using Google and read them on their phones. (In reality, they read summarised opinion pieces found via Google.)
A systematic reviewer may read papers in full. But even they scan papers for inclusion/exclusion criteria first. The deeper the information is buried, the greater the risk of misclassification. I'm not suggesting that TLDRs will fix this, it's just another data point in why we're seeing TLDRs being created.
* "Everybody" and "nobody" here excludes researchers :)
Obviously abstracts can include a content summary as well as bibliographic metadata, but not all do.
No idea if their approach is useful, but they are tackling a worthwhile problem.
When there are 1000+ papers every week in your field you need some advanced tools. It's hard to read everything, it's O(N).
Consider the paper's abstract: "We introduce TLDR generation, a new form of extreme summarization, for scientific papers. TLDR generation involves high source compression and requires expert background knowledge and understanding of complex domain-specific language. To facilitate study on this task, we introduce SciTLDR, a new multi-target dataset of 5.4K TLDRs over 3.2K papers. SciTLDR contains both author-written and expert-derived TLDRs, where the latter are collected using a novel annotation protocol that produces high-quality summaries while minimizing annotation burden. We propose CATTS, a simple yet effective learning strategy for generating TLDRs that exploits titles as an auxiliary training signal. CATTS improves upon strong baselines under both automated metrics and human evaluations. Data and code are publicly available at this https URL."
The algorithm summarizes it as:
“We introduce TLDR generation, a new form of extreme summarization, for scientific papers that produces high-quality summaries while minimizing annotation burden.”
We introduce SCITLDR, a new multi-target data set of 5.4KTLDRs over 3.2Kpapers.
Keeping pdf's copy-paste artifacts:
We introduceTLDRgeneration, a new formof extreme extreme summarization, for scientific pa-pers.
Adding intro and conclusion (optional):
We introduce SCITLDR, a new data set of 5.4KTLDRs over 3.2Kpapers.
I'm sticking with extractive approaches plus a bunch of hard-coded general and domain-specific rules for now.
(Just kidding.)
In fact I kind of like the way this is going since it represents a fantastic opportunity for NL researchers to stand out simply by publishing research and corpora focused exclusively on low-resource languages and non-English/Mandarin in general.
It is also important to note that most of the ML research in the field is pretty much language agnostic and is concerned with general concept such as efficient en-/decoding [1], training methods [2], and even stealing pre-trained weights from APIs (like GPT-2 or even 3) without paying for training [3] :)
It's just easier to get your hands on and verify English corpora, results and pre-trained models for reproducibility than say Mongolian or Gaelic so that's a factor, too.
[1] https://arxiv.org/pdf/1904.09751.pdf